16.7.2 Vehicle Motion Understanding and Analysis

Chapter Contents (Back)
In the subsections.

16.7.2.1 Vehicle Recognition, Car Recognition, Vehicle Detection

Chapter Contents (Back)
Vehicle Recognition. Vehicle Detection. Detailed identification:
See also Vehicle Make or Model or Type Recogniton.
See also Vehicle Pose.
See also Nighttime Vehicle Detection and Recognition.
See also Vehicle Counting.
See also Obstacle Dectection, Objects on the Road. For Aerial images:
See also ATR -- Vehicles, Aerial Images, Vehicle Detection.
See also Vehicle Detection, SAR. Primarily related to driving, other vehicles:
See also Other Vehicles.

MIT Car Database MITC,
Online2000
HTML Version. Dataset, Vehicles. BibRef 0001

PKU-VD Dataset,
2017 HTML Version.
Dataset, Vehicles. VD1: 1,097,649 images. 1,232 vehicle models and 11 colors. VD2: 807,260 images. 1,112 vehicle models and 11 colors. Reference:
See also Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles.

PKU VehicleID Dataset,
2016 HTML Version.
Dataset, Vehicles. 10319 vehicles, 90196 images. Reference:
See also Deep Relative Distance Learning: Tell the Difference between Similar Vehicles.

Tanaka, R.[Ryohei], Kitamura, A.[Akinobu], Odake, T.[Takaaki], Kato, Y.[Yutaka],
Optical vehicle detection system,
US_Patent4,433,325, 02/21/1984.
HTML Version. In a selected lane. BibRef 8402

Fujioka, A.[Arisa], Kageyama, S.[Satoshi],
Method for measuring the maximum gross weight of a motor vehicle,
US_Patent4,813,004, 03/14/1989
HTML Version. Measure the wheel diameter and compute. BibRef 8903

Michalopoulos, P.G.[Panos G.], Fundakowski, R.A.[Richard A.], Geokezas, M.[Meletios], Fitch, R.C.[Robert C.],
Vehicle detection through image processing for traffic surveillance and control,
US_Patent4,847,772, 07/11/1989.
HTML Version. Monitor section of road. BibRef 8907

Leung, M.K.[Mun K.], Huang, T.S.[Thomas S.],
Detecting the wheel pattern of a vehicle using stereo images,
PR(24), No. 12, 1991, pp. 1139-1151.
Elsevier DOI 0401
BibRef
Earlier:
Detecting wheels of vehicle in stereo images,
ICPR90(I: 263-267).
IEEE DOI 9006
Calculate the parameters of the plane containing wheels of the vehicle. Then transform any elliptical wheels contained in the plane to circular ones which can be extracted by the circle extraction algorithm. BibRef

Tan, T.N., Sullivan, G.D., Baker, K.D.,
Recognizing Objects on the Ground-Plane,
IVC(12), No. 3, April 1994, pp. 164-172.
Elsevier DOI BibRef 9404
Earlier:
Recognising Objects on the Ground Plane,
BMVC93(xx).
PDF File. BibRef
And: A1, A3, A2:
Structure from Motion Using Ground Plane Constraint,
ECCV92(277-281).
Springer DOI Reading Univ.
See also 3D Structure and Motion Estimation from 2D Image Sequences. BibRef

Tan, T.N., Sullivan, G.D., Baker, K.D.,
Closed-Form Algorithms for Object Pose and Scale Recovery in Constrained Scenes,
PR(29), No. 3, March 1996, pp. 449-461.
Elsevier DOI BibRef 9603
And:
Fast Algorithms for Object Orientation Determination,
SPIE(2488), 1995, pp. 263-273. BibRef
Earlier:
Pose Determination and Recognition of Vehicles in Traffic Scenes,
ECCV94(A:501-506).
Springer DOI BibRef
Earlier:
Linear Algorithms for Object Pose Estimation,
BMVC92(600-609).
PDF File. BibRef

Tan, T.N., Baker, K.D., Sullivan, G.D.,
Model-Independent Recovery of Object Orientations,
RA(13), No. 4, August 1997, pp. 602-606. 9708
BibRef
And: A1, A3, A2:
On Computing the Perspective Transformation Matrix and Camera Parameters,
BMVC93(125-134).
PDF File. BibRef

Du, L., Sullivan, G.D., Baker, K.D.,
3D Grouping by Viewpoint Consistency Ascent,
IVC(10), No. 5, June 1992, pp. 301-307.
Elsevier DOI BibRef 9206
Earlier: BMVC91(xx-yy).
PDF File. 9109
BibRef
And:
Quantitative Analysis of the Viewpoint Consistency Constraint in Model-Based Vision,
ICCV93(632-639).
IEEE DOI Match line models to the image. BibRef

Du, L., Sullivan, G.D., Baker, K.D.,
Modelling Data Complexity for Model-based Vision,
BMVC92(xx-yy).
PDF File. 9209
BibRef
And:
On Evidence Assessment for Model-based Recognition,
BMVC92(xx-yy).
PDF File. 9209
BibRef

Zhang, Z., Du, L., Sullivan, G.D., Baker, K.D.,
Model based 3D grouping by using 2D cues,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Marslin, R.F., Sullivan, G.D., Baker, K.D.,
Kalman Filters in Constrained Model Based Tracking,
BMVC91(xx-yy).
PDF File. 9109
BibRef

Worrall, A.D., Marslin, R.F., Sullivan, G.D., and Baker, K.D.,
Model-based Tracking,
BMVC91(xx).
PDF File. BibRef 9100

Sullivan, G.D., Baker, K.D., Worrall, A.D., Attwood, C.I., Remagnino, P.M.,
Model-based Vehicle Detection and Classification using Orthographic Approximations,
IVC(15), No. 8, August 1997, pp. 649-654.
Elsevier DOI 9708
BibRef
Earlier: BMVC96(Applications). 9608
University of Reading BibRef

Worrall, A.D., Sullivan, G.D., Baker, K.D.,
Advances in Model Based Traffic Vision,
BMVC93(559-569).
PDF File. (Reading Univ) BibRef 9300

Tan, T.N., Sullivan, G.D., Baker, K.D.,
Model-Based Localization and Recognition of Road Vehicles,
IJCV(27), No. 1, March 1998, pp. 5-25.
DOI Link 9805
BibRef
And:
Fast Vehicle Localization and Recognition without Line Extraction and Matching,
BMVC94(95-104).
PDF File. 9409
(Discrepancy in page number.)
See also Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles. BibRef

Zhang, Z.X.[Zhao-Xiang], Dong, W.S.[Wei-Shan], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
EDA Approach for Model Based Localization and Recognition of Vehicles,
VS07(1-8).
IEEE DOI 0706
BibRef

Tan, T.N.,
Locating and Recognizing Road Vehicles,
OptEng(37), No. 1, January 1998, pp. 202-207. 9802
BibRef

Tan, T.N., Baker, K.D.,
Efficient Image Gradient Based Vehicle Localization,
IP(9), No. 8, August 2000, pp. 1343-1356.
IEEE DOI 0008
BibRef

Tan, T.N., Sullivan, G.D., Baker, K.D.,
Efficient Image Gradient-Based Object Localization and Recognition,
CVPR96(397-402).
IEEE DOI Image gradients for vehicle models. BibRef 9600

Maybank, S.J., Worrall, A.D., Sullivan, G.D.,
Filter for Car Tracking Based on Acceleration and Steering Angle,
BMVC96(Poster Session 2). 9608
BibRef
And:
A Filter for Visual Tracking Based on a Stochastic Model for Driver Behaviour,
ECCV96(II:540-549).
Springer DOI University of Reading BibRef

Maybank, S.J.,
Filter based estimates of depth,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Sullivan, G.D., Worrall, A.D., Ferryman, J.M.,
Visual Object Recognition Using Deformable Models of Vehicles,
Context95(xx) BibRef 9500

Charkari, N.M., Mori, H.,
Visual Vehicle Detection and Tracking Based on the Sign Pattern,
AdvRob(9), No. 4, 1995, pp. 367-382. BibRef 9500

Dubuisson-Jolly, M.P.[Marie-Pierre], Lakshmanan, S., Jain, A.K.,
Vehicle Segmentation and Classification Using Deformable Templates,
PAMI(18), No. 3, March 1996, pp. 293-308.
IEEE DOI Tracking. BibRef 9603
Earlier:
Vehicle Segmentation Using Deformable Templates,
SCV95(581-586).
IEEE DOI Siemens Corporate Research. U. of Michigan Dearbon. Michigan State University. Given a simple polygonal model of the vehicle, find and track it. BibRef

Alves, J.F.[James F.], Cacnio, G.R.[Gerry R.], Stevens, D.R.[David R.],
Video image processor and method for detecting vehicles,
US_Patent5,535,314, Jul 9, 1996
WWW Link. BibRef 9607

Mantri, S., Bullock, D., Garrett, J.,
Vehicle Detection Using a Hardware-Implemented Neural-Net,
IEEE_Expert(12), No. 1, January/February 1997, pp. 15-21. 9703
BibRef

Kitamura, T.[Tadaaki], Kobayashi, Y.[Yoshiki], Nakanishi, K.[Kunio], Yahiro, M.[Masakazu], Satoh, Y.[Yoshiyuki], Shibata, T.[Toshiro], Horie, T.[Takeshi], Yamamoto, K.[Katsuyuki], Takatou, M.[Masao], Inoue, H.[Haruki], Asada, K.[Kazuyoshi],
Object recognition system and abnormality detection system using image processing,
US_Patent5,757,287, May 26, 1998.
HTML Version. BibRef 9805
Earlier: US_Patent5,554,983, September 10, 1996.
HTML Version. Use templates to find the parts of the vehicle. BibRef

Takatou, M.[Masao], Takahashi, K.[Kazunori], Hamada, N.[Nobuhiro], Kitamura, T.[Tadaaki], Kikuchi, K.[Kuniyuki], Takenaga, H.[Hiroshi], Morooka, Y.[Yasuo],
Traffic flow measuring method and apparatus,
US_Patent5,283,573, February 1, 1994,
HTML Version. BibRef 9402

Takatou, M., Onuma, C., Kobayashi, Y.,
Detection of Objects Including Persons Using Image Processing,
ICPR96(III: 466-472).
IEEE DOI 9608
(Hitachi Res. Laboratory, J) BibRef

Lai, A.H.S., Yung, N.H.C.,
Vehicle-Type Identification Through Automated Virtual Loop Assignment and Block-Based Direction-Biased Motion Estimation,
ITS(1), No. 2, June 2000, pp. 86-97.
IEEE Abstract. BibRef 0006

Ellis, R.D., Meitzler, T.J., Witus, G., Sohn, E., Bryk, D., Goetz, R., Gerhart, G.R.,
Computational Modeling of Age-Differences in a Visually Demanding Driving Task: Vehicle Detection,
SMC-A(30), No. 3, May 2000, pp. 336-346.
IEEE Top Reference. 0006
Evaluation, Vehicle Detection. Human performance. BibRef

Kagesawa, M., Ueno, S., Ikeuchi, K., Kashiwagi, H.,
Recognizing vehicles in infrared images using IMAP parallel vision board,
ITS(2), No. 1, March 2001, pp. 10-17.
IEEE Abstract. 0402
BibRef

Gupte, S., Masoud, O.T., Martin, R.F.K., Papanikolopoulos, N.P.,
Detection and classification of vehicles,
ITS(3), No. 1, March 2002, pp. 37-47.
IEEE Abstract. 0402
BibRef

Li, X.B.[Xiao-Bo], Liu, Z.Q.[Zhi-Qiang], Leung, K.M.[Ka-Ming],
Detection of vehicles from traffic scenes using fuzzy integrals,
PR(35), No. 4, April 2002, pp. 967-980.
Elsevier DOI 0201
BibRef

Setchell, C., Dagless, E.L.,
Vision-based road-traffic monitoring sensor,
VISP(148), No. 1, February 2001, pp. 78-84. 0105
BibRef

Bedenas, J., Boder, M., Pla, F.,
Segmenting Traffic Scenes from Grey Level and Motion Information,
PAA(4), No. 1, 2001, pp. 28-38.
Springer DOI 0105
BibRef

Kato, J.[Jien], Watanabe, T.[Toyohide], Joga, S.[Sébastien], Rittscher, J.[Jens], Blake, A.[Andrew],
An HMM-Based Segmentation Method for Traffic Monitoring Movies,
PAMI(24), No. 9, September 2002, pp. 1291-1296.
IEEE Abstract. 0209
Deal with shadows of moving ogjects. Classify each region as shadow, background, foreground. BibRef

Zhang, W.[Wei], Fang, X.Z.[Xiang Zhong], Yang, X.K.[Xiao-Kang],
Moving vehicles segmentation based on Bayesian framework for Gaussian motion model,
PRL(27), No. 9, July 2006, pp. 956-967.
Elsevier DOI Vehicles detection 0605
BibRef

Stojmenovic, M.[Milos],
Real Time Machine Learning Based Car Detection in Images With Fast Training,
MVA(17), No. 3, August 2006, pp. 163-172.
Springer DOI 0606
BibRef

Toulminet, G., Bertozzi, M., Mousset, S., Bensrhair, A., Broggi, A.,
Vehicle Detection by Means of Stereo Vision-Based Obstacles Features Extraction and Monocular Pattern Analysis,
IP(15), No. 8, August 2006, pp. 2364-2375.
IEEE DOI 0606

See also Pedestrian localization and tracking system with Kalman filtering. BibRef

Tsai, L.W., Hsieh, J.W.[Jun-Wei], Fan, K.C.,
Vehicle Detection Using Normalized Color and Edge Map,
IP(16), No. 3, March 2007, pp. 850-864.
IEEE DOI 0703
BibRef

Urazghildiiev, I., Ragnarsson, R., Ridderstrom, P., Rydberg, A., Ojefors, E., Wallin, K., Enochsson, P., Ericson, M., Lofqvist, G.,
Vehicle Classification Based on the Radar Measurement of Height Profiles,
ITS(8), No. 2, April 2007, pp. 245-253.
IEEE DOI 0706
BibRef

Lam, W.W.L., Pang, C.C.C., Yung, N.H.C.,
Vehicle-Component Identification Based on Multiscale Textural Couriers,
ITS(8), No. 4, December 2007, pp. 681-694.
IEEE DOI 0712
BibRef
Earlier:
Multi-scale space vehicle component identification,
ICIP04(II: 925-928).
IEEE DOI 0505
BibRef

Wang, C.C.R.[Chi-Chen Raxle], Lien, J.J.J.[Jenn-Jier James],
Automatic Vehicle Detection Using Local Features: A Statistical Approach,
ITS(9), No. 1, March 2008, pp. 83-96.
IEEE DOI 0803
BibRef
Earlier:
Automatic Vehicle Detection Using Statistical Approach,
ACCV06(II:171-182).
Springer DOI 0601
BibRef

Pang, C.C.C.[Clement Chun Cheong], Tan, Z.G.[Zhi-Gang], Yung, N.H.C.[Nelson Hon Ching],
A methodology for resolving severely occluded vehicles based on component-based multi-resolution relational graph matching,
ICMV07(141-146).
IEEE DOI 0712
BibRef

Grabner, H.[Helmut], Nguyen, T.T.[Thuy Thi], Gruber, B.[Barbara], Bischof, H.[Horst],
On-line boosting-based car detection from aerial images,
PandRS(63), No. 3, May 2008, pp. 382-396.
Elsevier DOI 0711
Award, ISPRS. Vehicle Detection. Car detection; Aerial image; Adaboost; On-line learning; Pattern recognition; UltraCamD BibRef

Grabner, H.[Helmut], Sochman, J.[Jan], Bischof, H.[Horst], Matas, J.G.[Jiri G.],
Training sequential on-line boosting classifier for visual tracking,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Grabner, M.[Michael], Zach, C.[Christopher], Bischof, H.[Horst],
Efficient Tracking as Linear Program on Weak Binary Classifiers,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Grabner, M.[Michael], Grabner, H.[Helmut], Bischof, H.[Horst],
Learning Features for Tracking,
CVPR07(1-8).
IEEE DOI 0706
BibRef
Earlier: A2, A1, A3:
Real-Time Tracking via On-line Boosting,
BMVC06(I:47).
PDF File. 0609

See also Conservative Visual Learning for Object Detection with Minimal Hand Labeling Effort.
See also On robustness of on-line boosting: a competitive study. BibRef

Grabner, H.[Helmut], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Eigenboosting: Combining Discriminative and Generative Information,
CVPR07(1-8).
IEEE DOI 0706
BibRef
Earlier: A1, A3, Only:
On-line Boosting and Vision,
CVPR06(I: 260-267).
IEEE DOI 0606
AdaBoost feature selection method. BibRef

Santner, J.[Jakob], Leistner, C.[Christian], Saffari, A.[Amir], Pock, T.[Thomas], Bischof, H.[Horst],
PROST: Parallel robust online simple tracking,
CVPR10(723-730).
IEEE DOI 1006
BibRef

Leistner, C.[Christian], Godec, M.[Martin], Schulter, S.[Samuel], Saffari, A.[Amir], Werlberger, M.[Manuel], Bischof, H.[Horst],
Improving classifiers with unlabeled weakly-related videos,
CVPR11(2753-2760).
IEEE DOI 1106
BibRef

Saffari, A.[Amir], Leistner, C.[Christian], Godec, M.[Martin], Bischof, H.[Horst],
Robust Multi-View Boosting with Priors,
ECCV10(III: 776-789).
Springer DOI 1009
BibRef

Saffari, A.[Amir], Godec, M.[Martin], Pock, T.[Thomas], Leistner, C.[Christian], Bischof, H.[Horst],
Online multi-class LPBoost,
CVPR10(3570-3577).
IEEE DOI 1006

See also On robustness of on-line boosting: a competitive study.
See also On-Line Multi-view Forests for Tracking. BibRef

Godec, M.[Martin], Sternig, S.[Sabine], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Context-driven clustering by multi-class classification in an active learning framework,
UCVP10(19-24).
IEEE DOI 1006
BibRef

Saffari, A.[Amir], Leistner, C.[Christian], Bischof, H.[Horst],
Regularized multi-class semi-supervised boosting,
CVPR09(967-974).
IEEE DOI 0906
BibRef

Grabner, H.[Helmut], Leistner, C.[Christian], Bischof, H.[Horst],
Semi-supervised On-Line Boosting for Robust Tracking,
ECCV08(I: 234-247).
Springer DOI 0810
Award, Koenderink Prize. BibRef
Earlier: A2, A1, A3:
Semi-supervised boosting using visual similarity learning,
CVPR08(1-8).
IEEE DOI 0806
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Leistner, C., Roth, P.M., Grabner, H., Bischof, H., Starzacher, A., Rinner, B.,
Visual on-line learning in distributed camera networks,
ICDSC08(1-10).
IEEE DOI 0809
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Arth, C.[Clemens], Leistner, C.[Christian], Bischof, H.[Horst],
Object Reacquisition and Tracking in Large-Scale Smart Camera Networks,
ICDSC07(156-163).
IEEE DOI 0709
BibRef
Earlier: A1, A3, A2:
TRICam: An Embedded Platform for Remote Traffic Surveillance,
EmbedCV06(125).
IEEE DOI 0609
BibRef

Saffari, A.[Amir], Grabner, H.[Helmut], Bischof, H.[Horst],
SERBoost: Semi-supervised Boosting with Expectation Regularization,
ECCV08(III: 588-601).
Springer DOI 0810
BibRef

Jia, Y.Q.[Yang-Qing], Zhang, C.S.[Chang-Shui],
Front-view vehicle detection by Markov chain Monte Carlo method,
PR(42), No. 3, March 2009, pp. 313-321.
Elsevier DOI 0811
Vehicle detection; Bayesian method; Maximizing a posteriori; Markov chain Monte Carlo BibRef

Lamosa, F.[Francisco], Hu, Z.C.[Zhen-Cheng], Uchimura, K.[Keiichi],
Vehicle Detection Using Multi-level Probability Fusion Maps Generated by a Multi-camera System,
AVSBS08(10-17).
IEEE DOI 0809
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Zhu, Z.F.[Zhen-Feng], Lu, H.Q.[Han-Qing], Hu, J., Uchimura, K.,
Car detection based on multi-cues integration,
ICPR04(II: 699-702).
IEEE DOI 0409
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Ponsa, D.[Daniel], Lopez, A.M.[Antonio M.],
Variance reduction techniques in particle-based visual contour tracking,
PR(42), No. 11, November 2009, pp. 2372-2391.
Elsevier DOI 0907
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And:
Cascade of Classifiers for Vehicle Detection,
ACIVS07(980-989).
Springer DOI 0708
BibRef
Earlier:
Vehicle Trajectory Estimation Based on Monocular Vision,
IbPRIA07(I: 587-594).
Springer DOI 0706
Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling BibRef

Thomas, A.[Alexander], Ferrari, V.[Vittorio], Leibe, B.[Bastian], Tuytelaars, T.[Tinne], Van Gool, L.J.[Luc J.],
Shape-from-recognition: Recognition enables meta-data transfer,
CVIU(113), No. 12, Decmeber 2009, pp. 1222-1234,.
Elsevier DOI 0911
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Earlier:
Depth-From-Recognition: Inferring Meta-data by Cognitive Feedback,
ICCV07(1-8).
IEEE DOI 0710
Object recognition; Shape-from-X Infer low level cues from high level information. Given a vehicle, infer the shape.
See also Robust Object Detection with Interleaved Categorization and Segmentation. BibRef

Thomas, A.[Alexander], Ferrar, V.[Vittorio], Leibe, B.[Bastian], Tuytelaars, T.[Tinne], Schiel, B.[Bernt], Van Gool, L.J.[Luc J.],
Towards Multi-View Object Class Detection,
CVPR06(II: 1589-1596).
IEEE DOI 0606
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Leibe, B.[Bastian], Mikolajczyk, K.[Krystian], Schiele, B.[Bernt],
Segmentation Based Multi-Cue Integration for Object Detection,
BMVC06(III:1169).
PDF File. 0609
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And: A2, A1, A3:
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CVPR06(I: 26-36).
IEEE DOI 0606

See also Efficient Clustering and Matching for Object Class Recognition. BibRef

Shan, Y.[Ying], Sawhney, H.S.[Harpreet S.], Kumar, R.T.[Rakesh Teddy],
Unsupervised Learning of Discriminative Edge Measures for Vehicle Matching between Non-Overlapping Cameras,
PAMI(30), No. 4, April 2008, pp. 700-711.
IEEE DOI 0803
BibRef
Earlier: CVPR05(I: 894-901).
IEEE DOI 0507
BibRef
And:
Vehicle Identification between Non-Overlapping Cameras without Direct Feature Matching,
ICCV05(I: 378-385).
IEEE DOI 0510
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Guo, Y.L.[Yan-Lin], Kumar, R.[Rakesh], Sawhney, H.S.[Harpreet S.],
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US_Patent6,353,678, Mar 5, 2002
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Guo, Y.L.[Yan-Lin], Rao, C.[Cen], Samarasekera, S.[Supun], Kim, J.[Janet], Kumar, R.[Rakesh], Sawhney, H.S.[Harpreet S.],
Matching vehicles under large pose transformations using approximate 3D models and piecewise MRF model,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Guo, Y.L.[Yan-Lin], Shan, Y.[Ying], Sawhney, H.S.[Harpreet S.], Kumar, R.T.[Rakesh T.],
PEET: Prototype Embedding and Embedding Transition for Matching Vehicles over Disparate Viewpoints,
CVPR07(1-8).
IEEE DOI 0706
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Morris, B.T.[Brendan T.], Trivedi, M.M.[Mohan M.],
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance,
CirSysVideo(18), No. 8, August 2008, pp. 1114-1127.
IEEE DOI 0809
Survey, Trajectory Analysis. BibRef

Morris, B.T.[Brendan T.], Trivedi, M.M.[Mohan M.],
Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach,
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IEEE DOI 1110
BibRef
Earlier:
Learning trajectory patterns by clustering: Experimental studies and comparative evaluation,
CVPR09(312-319).
IEEE DOI 0906
BibRef

Morris, B.T.[Brendan T.], Trivedi, M.M.[Mohan M.],
Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video,
ITS(9), No. 3, September 2008, pp. 425-437.
IEEE DOI 0809
BibRef
Earlier:
Improved Vehicle Classification in Long Traffic Video by Cooperating Tracker and Classifier Modules,
AVSBS06(9-9).
IEEE DOI 0611
BibRef

Morris, B.T.[Brendan T.], Trivedi, M.M.[Mohan M.],
Contextual Activity Visualization from Long-Term Video Observations,
IEEE_Int_Sys(25), No. 3, May-June 2010, pp. 50-62.
IEEE DOI 1007
BibRef
Earlier:
Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis,
AVSBS08(154-161).
IEEE DOI 0809

See also Commentary Paper on Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis. BibRef

Guo, F.[Feng], Chellappa, R.[Rama],
Video Metrology Using a Single Camera,
PAMI(32), No. 7, July 2010, pp. 1329-1335.
IEEE DOI 1006
BibRef
Earlier:
Video Mensuration Using a Stationary Camera,
ECCV06(III: 164-176).
Springer DOI 0608
Measure line on plane (wheel-base). Uncalibrated camera, stationary or planar motion. BibRef

Wang, P.J.[Pao-Jen], Li, C.M.[Chi-Min], Wu, C.Y.[Cheng-Ying], Li, H.J.[Hsueh-Jyh],
A Channel Awareness Vehicle Detector,
ITS(11), No. 2, June 2010, pp. 339-347.
IEEE DOI 1007
BibRef

Leotta, M.J.[Matthew J.], Mundy, J.L.[Joseph L.],
Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model,
PAMI(33), No. 7, July 2011, pp. 1457-1469.
IEEE DOI 1106
BibRef
Earlier:
Predicting high resolution image edges with a generic, adaptive, 3-D vehicle model,
CVPR09(1311-1318).
IEEE DOI 0906
BibRef

Faro, A., Giordano, D., Spampinato, C.,
Adaptive Background Modeling Integrated With Luminosity Sensors and Occlusion Processing for Reliable Vehicle Detection,
ITS(12), No. 4, December 2011, pp. 1398-1412.
IEEE DOI 1112
BibRef

Feris, R.S.[Rogerio S.], Siddiquie, B.[Behjat], Petterson, J.[James], Zhai, Y., Datta, A.[Ankur], Brown, L.M.[Lisa M.], Pankanti, S.[Sharath],
Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos,
MultMed(14), No. 1, January 2012, pp. 28-42.
IEEE DOI 1201
BibRef
Earlier: A1, A3, A2, A6, A7, Only:
Large-scale vehicle detection in challenging urban surveillance environments,
WACV11(527-533).
IEEE DOI 1101
BibRef
Earlier: A1, A2, A4, A3, A6, A7, Only:
Attribute-based vehicle search in crowded surveillance videos,
ICMR11(18).
DOI Link 1301
user specifies a set of vehicle characteristics (such as color, direction of travel, speed, length, height, etc.) BibRef

Fan, Q.F.[Quan-Fu], Pankanti, S.[Sharath], Brown, L.[Lisa],
Long-term object tracking for parked vehicle detection,
AVSS14(223-229)
IEEE DOI 1411
Feature extraction BibRef

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Traffic surveillance system; Motion detection; Motion estimation; Motion compensation; Background subtraction; Swinging trees filtering; Raindrops filtering; Shadow elimination BibRef

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IEEE DOI 1206
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Wang, S.[Shuang], Cui, L.J.[Li-Juan], Liu, D.C.[Dian-Chao], Huck, R., Verma, P., Sluss, J.J., Cheng, S.,
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IEEE DOI 1206
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Teoh, S.S.[Soo Siang], Bräunl, T.[Thomas],
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Mithun, N.C., Rashid, N.U., Rahman, S.M.M.,
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IEEE DOI 1209
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Cheon, M., Lee, W., Yoon, C., Park, M.,
Vision-Based Vehicle Detection System With Consideration of the Detecting Location,
ITS(13), No. 3, September 2012, pp. 1243-1252.
IEEE DOI 1209
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McDonald, G.J., Ellis, J.S., Penney, R.W., Price, R.W.,
Real-Time Vehicle Identification Performance Using FPGA Correlator Hardware,
ITS(13), No. 4, December 2012, pp. 1891-1895.
IEEE DOI 1212
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Lin, Y.L., Tsai, M.K., Hsu, W.H., Chen, C.W.,
Investigating 3-D Model and Part Information for Improving Content-Based Vehicle Retrieval,
CirSysVideo(23), No. 3, March 2013, pp. 401-413.
IEEE DOI 1303
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Liu, L.W.[Li-Wei], Xing, J.L.[Jun-Liang], Duan, G.Q.[Gen-Quan], Ai, H.Z.[Hai-Zhou],
Scene transformation for detector adaptation,
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Elsevier DOI 1312
Vehicle detection BibRef

León, L.C.[Leissi Castañeda], Hirata, Jr., R.[Roberto],
Car detection in sequences of images of urban environments using mixture of deformable part models,
PRL(39), No. 1, 2014, pp. 39-51.
Elsevier DOI 1402
Mixture of deformable part models BibRef

Tian, B., Li, Y., Li, B., Wen, D.,
Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance,
ITS(15), No. 2, April 2014, pp. 597-606.
IEEE DOI 1404
Color BibRef

Li, B.[Bo], Song, X.[Xi], Wu, T.F.[Tian-Fu], Hu, W.Z.[Wen-Ze], Pei, M.T.[Ming-Tao],
Coupling-and-decoupling: A hierarchical model for occlusion-free object detection,
PR(47), No. 10, 2014, pp. 3254-3264.
Elsevier DOI 1406
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Earlier: A1, A3, A4, A5, Only:
Coupling-and-Decoupling: A Hierarchical Model for Occlusion-Free Car Detection,
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Springer DOI 1304
Occlusion modeling BibRef

Mangai, M.A., Gounden, N.A.,
Principal component analysis-based learning for preceding vehicle classification,
IET-ITS(8), No. 1, February 2014, pp. 28-35.
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image classification BibRef

Ambardekar, A.[Amol], Nicolescu, M.[Mircea], Bebis, G.N.[George N.], Nicolescu, M.[Monica],
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Survey, Vehicle Classification. BibRef

Chen, P.[Pan], Bai, X.[Xiang], Liu, W.Y.[Wen-Yu],
Vehicle Color Recognition on Urban Road by Feature Context,
ITS(15), No. 5, October 2014, pp. 2340-2346.
IEEE DOI 1410
automobiles BibRef

Lee, K.H.[Kuan-Hui], Hwang, J.N.[Jenq-Neng], Chen, S.I.[Shih-I],
Model-Based Vehicle Localization Based on 3-D Constrained Multiple-Kernel Tracking,
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IEEE DOI 1502
mobile radio BibRef

Wen, X., Shao, L., Fang, W., Xue, Y.,
Efficient Feature Selection and Classification for Vehicle Detection,
CirSysVideo(25), No. 3, March 2015, pp. 508-517.
IEEE DOI 1503
Educational institutions BibRef

Rao, Y.[Yunbo],
Automatic vehicle recognition in multiple cameras for video surveillance,
VC(31), No. 3, March 2015, pp. 271-280.
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Wu, T.F.[Tian-Fu], Zhu, S.C.[Song-Chun],
Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection,
PAMI(37), No. 5, May 2015, pp. 1013-1027.
IEEE DOI 1504
Accuracy BibRef
Earlier: ICCV13(753-760)
IEEE DOI 1403
Cost-Sensitive Computing BibRef

Wu, T.F.[Tian-Fu], Li, B.[Bo], Zhu, S.C.[Song-Chun],
Learning And-Or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation,
PAMI(38), No. 9, September 2016, pp. 1829-1843.
IEEE DOI 1609
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Earlier: A2, A1, A3:
Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model,
ECCV14(VI: 652-667).
Springer DOI 1408
CAD
See also Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs, A. BibRef

Vaddi, R.S., Boggavarapu, L.N., Anne, K.R.,
Computer Vision based Vehicle Recognition on Indian Roads,
IJCVSP(5), No. 1, 2015, pp. xx-yy.
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Park, J.H.[Jae-Hyuck], Tai, Y.W.[Yu-Wing],
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Elsevier DOI 1506
On-road vehicle motion prediction BibRef

Cho, H.M.[Han-Min], Hwang, S.Y.[Sun-Young],
High-performance on-road vehicle detection with non-biased cascade classifier by weight-balanced training,
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Li, Y., Er, M.J., Shen, D.,
A Novel Approach for Vehicle Detection Using an AND-OR-Graph-Based Multiscale Model,
ITS(16), No. 4, August 2015, pp. 2284-2289.
IEEE DOI 1508
Feature extraction BibRef

Ohn-Bar, E.[Eshed], Trivedi, M.M.[Mohan M.],
Learning to Detect Vehicles by Clustering Appearance Patterns,
ITS(16), No. 5, October 2015, pp. 2511-2521.
IEEE DOI 1511
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Earlier:
Fast and Robust Object Detection Using Visual Subcategories,
IWMV14(179-184)
IEEE DOI 1409
feature extraction. multiview vehicle detection
See also Multi-scale volumes for deep object detection and localization. BibRef

Ramirez, A.[Alfredo], Ohn-Bar, E.[Eshed], Trivedi, M.M.[Mohan M.],
Go with the Flow: Improving Multi-view Vehicle Detection with Motion Cues,
ICPR14(4140-4145)
IEEE DOI 1412
Adaptive optics BibRef

Achler, O., Trivedi, M.M.,
Vehicle wheel detector using 2D filter banks,
IVS04(25-30).
IEEE DOI 0411
Detect vehicles from moving vehicle. Find the wheels. Omnidirectional camera. BibRef

Satzoda, R.K.[Ravi Kumar], Trivedi, M.M.[Mohan M.],
Multipart Vehicle Detection Using Symmetry-Derived Analysis and Active Learning,
ITS(17), No. 4, April 2016, pp. 926-937.
IEEE DOI 1604
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See also On Enhancing Lane Estimation Using Contextual Cues. BibRef

Hu, C.P.[Chuan-Ping], Bai, X.[Xiang], Qi, L.[Li], Chen, P.[Pan], Xue, G.J.[Geng-Jian], Mei, L.[Lin],
Vehicle Color Recognition With Spatial Pyramid Deep Learning,
ITS(16), No. 5, October 2015, pp. 2925-2934.
IEEE DOI 1511
convolution BibRef

Hu, C.P.[Chuan-Ping], Bai, X.[Xiang], Qi, L.[Li], Wang, X., Xue, G.J.[Geng-Jian], Mei, L.[Lin],
Learning Discriminative Pattern for Real-Time Car Brand Recognition,
ITS(16), No. 6, December 2015, pp. 3170-3181.
IEEE DOI 1512
Image classification BibRef

Kim, J., Baek, J., Kim, E.,
A Novel On-Road Vehicle Detection Method Using pi-HOG,
ITS(16), No. 6, December 2015, pp. 3414-3429.
IEEE DOI 1512
Bayes methods BibRef

Cinaroglu, I.[Ibrahim], Bastanlar, Y.L.[Ya-Lin],
A direct approach for object detection with catadioptric omnidirectional cameras,
SIViP(10), No. 2, February 2016, pp. 413-420.
Springer DOI 1601
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Noh, S.[Seung_Jong], Shim, D., Jeon, M.[Moongu],
Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments,
ITS(17), No. 2, February 2016, pp. 323-335.
IEEE DOI 1602
Adaptation models BibRef

Noh, S.[Seung_Jong], Jeon, M.[Moongu],
Vehicle Detection Using Local Size-Specific Classifiers,
IEICE(E99-D), No. 9, September 2016, pp. 2351-2359.
WWW Link. 1609
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Zhuang, X., Kang, W., Wu, Q.,
Real-time vehicle detection with foreground-based cascade classifier,
IET-IPR(10), No. 4, 2016, pp. 289-296.
DOI Link 1604
Haar transforms BibRef

Hu, Q., Paisitkriangkrai, S.[Sakrapee], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.], Porikli, F.M.,
Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework,
ITS(17), No. 4, April 2016, pp. 1002-1014.
IEEE DOI 1604
Australia BibRef

Nieto, M., Vélez, G., Otaegui, O., Gaines, S., van Cutsem, G.,
Optimising computer vision based ADAS: vehicle detection case study,
IET-ITS(10), No. 3, 2016, pp. 157-164.
DOI Link 1604
computer vision BibRef

Zhang, Y., Zhao, C., He, J., Chen, A.,
Vehicles detection in complex urban traffic scenes using Gaussian mixture model with confidence measurement,
IET-ITS(10), No. 6, 2016, pp. 445-452.
DOI Link 1608
Gaussian processes BibRef

Yang, D., Park, H.,
A New Shape Feature for Vehicle Classification in Thermal Video Sequences,
CirSysVideo(26), No. 7, July 2016, pp. 1363-1375.
IEEE DOI 1608
edge detection BibRef

Lin, Y.B.[Yen-Bor], Young, C.P.[Chung-Ping],
High-precision bicycle detection on single side-view image based on the geometric relationship,
PR(63), No. 1, 2017, pp. 334-354.
Elsevier DOI 1612
Bicycle BibRef

Fairley, P.,
Self-driving cars have a bicycle problem,
Spectrum(54), No. 3, March 2017, pp. 12-13.
IEEE DOI 1703
News item. BibRef

Suhr, J.K., Jang, J., Min, D., Jung, H.G.,
Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments,
ITS(18), No. 5, May 2017, pp. 1078-1086.
IEEE DOI 1705
Cameras, Feature extraction, Global Positioning System, Roads, Urban areas, Vehicles, Wheels, GPS, IMU, Vehicle localization, digital map, particle filter, road marking, sensor, fusion BibRef

Yuan, X., Cao, X., Hao, X., Chen, H., Wei, X.,
Vehicle Detection by a Context-Aware Multichannel Feature Pyramid,
SMCS(47), No. 7, July 2017, pp. 1348-1357.
IEEE DOI 1706
Cameras, Feature extraction, Gray-scale, Image color analysis, Transforms, Vehicle detection, Vehicles, Context-aware feature, context-aware structural feature, scale invariant, vehicle, detection BibRef

Belgiovane, D.J., Chen, C.C., Chien, S.Y.P., Sherony, R.,
Surrogate Bicycle Design for Millimeter-Wave Automotive Radar Pre-Collision Testing,
ITS(18), No. 9, September 2017, pp. 2413-2422.
IEEE DOI 1709
bicycles, road safety, precollision systems, automotive radar, collision avoidance, pedestrian detection BibRef

Aqel, S., Hmimid, A., Sabri, M.A., Aarab, A.,
Road traffic: Vehicle detection and classification,
ISCV17(1-5)
IEEE DOI 1710
image motion analysis, object detection, road traffic, background subtraction approach, Shadow detection and removal. BibRef

Zhuo, L.[Li], Jiang, L.Y.[Li-Ying], Zhu, Z.Q.[Zi-Qi], Li, J.F.[Jia-Feng], Zhang, J.[Jing], Long, H.X.[Hai-Xia],
Vehicle classification for large-scale traffic surveillance videos using Convolutional Neural Networks,
MVA(28), No. 7, October 2017, pp. 793-802.
Springer DOI 1710
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Liu, M.[Meng], Hua, W.[Wang], Wei, Q.[Quan],
Vehicle detection using three-axis AMR sensors deployed along travel lane markings,
IET-ITS(11), No. 9, November 2017, pp. 581-587.
DOI Link 1710
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Chu, W., Liu, Y., Shen, C., Cai, D., Hua, X.S.,
Multi-Task Vehicle Detection With Region-of-Interest Voting,
IP(27), No. 1, January 2018, pp. 432-441.
IEEE DOI 1712
feedforward neural nets, learning (artificial intelligence), object detection, region-of-interest BibRef

Yang, Z.[Zi], Pun-Cheng, L.S.C.[Lilian S.C.],
Vehicle detection in intelligent transportation systems and its applications under varying environments: A review,
IVC(69), 2018, pp. 143-154.
Elsevier DOI 1802
Survey, Vehicle Detection. Vehicle detection, Intelligent Transportation Systems, Varying environments, Traffic surveillance BibRef

Bai, S.[Shuang], Liu, Z.Y.[Zhen-Yao], Yao, C.[Chang],
Classify vehicles in traffic scene images with deformable part-based models,
MVA(29), No. 3, April 2018, pp. 393-403.
WWW Link. 1804
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Zhou, Y., Liu, L., Shao, L., Mellor, M.,
Fast Automatic Vehicle Annotation for Urban Traffic Surveillance,
ITS(19), No. 6, June 2018, pp. 1973-1984.
IEEE DOI 1806
Image color analysis, Proposals, Real-time systems, Surveillance, Training, Vehicle detection, Vehicle detection, latent knowledge guidance BibRef

Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Smoky vehicle detection based on multi-scale block Tamura features,
SIViP(12), No. 6, September 2018, pp. 1061-1068.
WWW Link. 1808
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Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Automatic smoky vehicle detection from traffic surveillance video based on vehicle rear detection and multi-feature fusion,
IET-ITS(13), No. 2, February 2019, pp. 252-259.
DOI Link 1902
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Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Contour-based smoky vehicle detection from surveillance video for alarm systems,
SIViP(13), No. 2, March 2019, pp. 217-225.
WWW Link. 1904
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Chen, J., Xu, W., Xu, H., Lin, F., Sun, Y., Shi, X.,
Fast Vehicle Detection Using a Disparity Projection Method,
ITS(19), No. 9, September 2018, pp. 2801-2813.
IEEE DOI 1809
Feature extraction, Vehicle detection, Robustness, Stereo vision, Lighting, Cameras, Roads, Stereo vision, disparity feature, reusing of fourier transformation BibRef

Tu, C.L.[Chun-Ling], Du, S.Z.[Sheng-Zhi],
A Hough Space Feature for Vehicle Detection,
ISVC18(147-156).
Springer DOI 1811
BibRef

Ding, L.[Lu], Wang, Y.[Yong], Laganière, R.[Robert], Luo, X.B.[Xin-Bin], Fu, S.[Shan],
Scale-Aware RPN for Vehicle Detection,
ISVC18(487-499).
Springer DOI 1811
BibRef

Zhang, Y.S.[Yun-Sheng], Zhao, C.H.[Chi-Hang], Shi, W.[Wen], Leng, K.J.[Kai-Jun],
Vehicles detection for illumination changes urban traffic scenes employing adaptive local texture feature background model,
IET-ITS(12), No. 10, December 2018, pp. 1283-1290.
DOI Link 1812
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OBrien, E.J.[Eugene J.], Caprani, C.C.[Colin C.], Blacoe, S.[Serena], Guo, D.[Dong], Malekjafarian, A.[Abdollah],
Detection of vehicle wheels from images using a pseudo-wavelet filter for analysis of congested traffic,
IET-IPR(12), No. 12, December 2018, pp. 2222-2228.
DOI Link 1812
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Liang, J.[Jun], Chen, X.[Xu], He, M.L.[Mei-Ling], Chen, L.[Long], Cai, T.[Tao], Zhu, N.[Ning],
Car detection and classification using cascade model,
IET-ITS(12), No. 10, December 2018, pp. 1201-1209.
DOI Link 1812
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Hu, X., Xu, X., Xiao, Y., Chen, H., He, S., Qin, J., Heng, P.,
SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection,
ITS(20), No. 3, March 2019, pp. 1010-1019.
IEEE DOI 1903
Vehicle detection, Feature extraction, Proposals, Object detection, Videos, Convolutional neural networks, Computer science, intelligent transportation system BibRef

Kim, C.Y.[Chang-Yon], Gwak, J.[Jeonghwan], Shim, D.[Daeyoung], Jeon, M.[Moongu],
A framework for automatically constructing a dataset for training a vehicle detector,
IJCVR(9), No. 2, 2019, pp. 192-206.
DOI Link 1904
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Huang, Y.[Yu], Zhou, Z.H.[Zhi-Heng], Wang, T.L.[Tian-Lei], Cao, Q.[QiAn], Huang, J.C.[Jun-Chu], Chen, Z.R.[Zi-Rong],
A Part-Based Gaussian Reweighted Approach for Occluded Vehicle Detection,
IEICE(E102-D), No. 5, May 2019, pp. 1097-1101.
WWW Link. 1906
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Wang, Y.[Ye], Deng, W.W.[Wei-Wen], Liu, Z.Y.[Zhen-Yi], Wang, J.S.[Jin-Song],
Deep learning-based vehicle detection with synthetic image data,
IET-ITS(13), No. 7, July 2019, pp. 1097-1105.
DOI Link 1906
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Tong, G.F.[Guo-Feng], Chen, H.R.[Huai-Rong], Li, Y.[Yong], Du, X.[Xiance], Zhang, Q.C.[Qing-Chun],
Object detection for panoramic images based on MS-RPN structure in traffic road scenes,
IET-CV(13), No. 5, August 2019, pp. 500-506.
DOI Link 1908
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Bibars, A.[Ahmed], Mahroos, M.[Mohsen],
New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation,
IET-CV(13), No. 5, August 2019, pp. 443-451.
DOI Link 1908
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Liu, W., Liao, S., Hu, W.,
Perceiving Motion From Dynamic Memory for Vehicle Detection in Surveillance Videos,
CirSysVideo(29), No. 12, December 2019, pp. 3558-3567.
IEEE DOI 1912
Videos, Feature extraction, Object detection, Detectors, Surveillance, Proposals, Dynamics, Object detection, deep neural network BibRef

Husain, A.A.[Agha Asim], Maity, T.[Tanmoy], Yadav, R.K.[Ravindra Kumar],
Vehicle detection in intelligent transport system under a hazy environment: a survey,
IET-IPR(14), No. 1, January 2020, pp. 1-10.
DOI Link 1912
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Teng, S.Z.[Shang-Zhi], Zhang, S.L.[Shi-Liang], Huang, Q.M.[Qing-Ming], Sebe, N.[Nicu],
Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification,
IJCV(129), No. 3, March 2021, pp. 719-735.
Springer DOI 2103
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Xu, K.[Ke], Gong, H.[Hua], Liu, F.[Fang],
Vehicle detection based on improved multitask cascaded convolutional neural network and mixed image enhancement,
IET-IPR(14), No. 17, 24 December 2020, pp. 4621-4632.
DOI Link 2104
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Boukerche, A.[Azzedine], Hou, Z.J.[Zhi-Jun],
Object Detection Using Deep Learning Methods in Traffic Scenarios,
Surveys(54), No. 2, March 2021, pp. xx-yy.
DOI Link 2104
convolutional neural networks, autonomous driving system, Object detection, vehicle detection, deep learning BibRef

Li, D.L.[Dong Lin], Prasad, M.[Mukesh], Liu, C.L.[Chih-Ling], Lin, C.T.[Chin-Teng],
Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning,
ITS(22), No. 5, May 2021, pp. 3146-3157.
IEEE DOI 2105
Vehicle detection, Image color analysis, Roads, Transforms, Feature extraction, Robustness, Deformable models, color transformation BibRef

Zhao, M.[Min], Zhong, Y.[Yuan], Sun, D.[Dihua], Chen, Y.H.[Yu-Hao],
Accurate and efficient vehicle detection framework based on SSD algorithm,
IET-IPR(15), No. 13, 2021, pp. 3094-3104.
DOI Link 2110
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Chen, G.[Guang], Wang, F.[Fa], Qu, S.Q.[San-Qing], Chen, K.[Kai], Yu, J.W.[Jun-Wei], Liu, X.Y.[Xiang-Yong], Xiong, L.[Lu], Knoll, A.[Alois],
Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods,
ITS(22), No. 12, December 2021, pp. 7699-7711.
IEEE DOI 2112
Laser radar, Robot sensing systems, intelligent transportation system BibRef

Zhang, J.Y.[Jin-Yu], Zhang, Y.F.[Yi-Fan],
Vehicular Localization Based on CSI-Fingerprint and Vector Match,
ITS(22), No. 12, December 2021, pp. 7736-7746.
IEEE DOI 2112
OFDM, Roads, Global Positioning System, Signal processing algorithms, filter BibRef

Luo, X.Y.[Xiao-Yue], Wang, Y.H.[Yan-Hui], Cai, B.[Benhe], Li, Z.X.[Zhan-Xing],
Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
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Tian, Y.[Yan], Chen, T.[Tao], Cheng, G.H.[Guo-Hua], Yu, S.H.[Shi-Hao], Li, X.[Xi], Li, J.Y.[Jian-Yuan], Yang, B.[Bailin],
Global Context Assisted Structure-Aware Vehicle Retrieval,
ITS(23), No. 1, January 2022, pp. 165-174.
IEEE DOI 2201
Semantics, Convolution, Image retrieval, Vehicle detection, Space vehicles, Image retrieval, deep learning, landmark alignment, intelligent transportation system BibRef

Shen, B.[Bo], Zhang, R.[Rui], Chen, H.[Hao],
An Adaptively Attention-Driven Cascade Part-Based Graph Embedding Framework for UAV Object Re-Identification,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Zheng, M.X.[Ming-Xue], Wu, H.Y.[Hua-Yi],
Vehicle Recognition Based on Region Growth of Relative Tension and Similarity Measurement of Side Projection Profile of Vehicle Body,
RS(15), No. 6, 2023, pp. 1493.
DOI Link 2304
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Oh, D.[Dahyun], Kang, K.[Kyubyung], Seo, S.[Sungchul], Xiao, J.[Jinwu], Jang, K.[Kyochul], Kim, K.[Kibum], Park, H.[Hyungkeun], Won, J.[Jeonghun],
Low-Cost Object Detection Models for Traffic Control Devices through Domain Adaption of Geographical Regions,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
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Roy, D.[Debashri], Li, Y.Y.[Yuan-Yuan], Jian, T.[Tong], Tian, P.[Peng], Chowdhury, K.[Kaushik], Ioannidis, S.[Stratis],
Multi-Modality Sensing and Data Fusion for Multi-Vehicle Detection,
MultMed(25), 2023, pp. 2280-2295.
IEEE DOI 2306
Radar tracking, Sensors, Radar imaging, Radar, Acoustics, Visualization, Sensor fusion, Vehicle detection, tracking, radar BibRef

Lu, Y.F.[Yan-Feng], Gao, J.W.[Jing-Wen], Yu, Q.[Qian], Li, Y.[Yi], Lv, Y.S.[Yi-Sheng], Qiao, H.[Hong],
A Cross-Scale and Illumination Invariance-Based Model for Robust Object Detection in Traffic Surveillance Scenarios,
ITS(24), No. 7, July 2023, pp. 6989-6999.
IEEE DOI 2307
Feature extraction, Object detection, Lighting, Traffic control, Adaptation models, Task analysis, Robustness, Traffic detection, illumination invariance BibRef

Luo, T.[Tong], Wang, H.[Hai], Cai, Y.F.[Ying-Feng], Chen, L.[Long], Wang, K.[Kuan], Yu, Y.J.[Yi-Jie],
Binary residual feature pyramid network: An improved feature fusion module based on double-channel residual pyramid structure for autonomous detection algorithm,
IET-ITS(17), No. 7, 2023, pp. 1288-1301.
DOI Link 2307
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Chen, C.[Chen], Wang, C.Y.[Chen-Yu], Liu, B.[Bin], He, C.[Ci], Cong, L.[Li], Wan, S.H.[Shao-Hua],
Edge Intelligence Empowered Vehicle Detection and Image Segmentation for Autonomous Vehicles,
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Image transformation, Domain adaptation, Attention mechanism, Object detection BibRef

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IEEE DOI 2402
Cameras, Standards, Automotive engineering, Streaming media, Video compression, Training, Propagation losses, Compression, ADAS BibRef

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AICity23(5419-5427)
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IEEE DOI 2309
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PRL(167), 2023, pp. 45-52.
Elsevier DOI 2303
Object detection, Small scale, Super-resolution, Convolutional neural networks BibRef

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SOCAR: Socially-Obtained CAR Dataset for Image Recognition in the Wild,
Novelty23(430-438)
IEEE DOI 2302
Image recognition, Computational modeling, Surveillance, Conferences, Reproducibility of results, Automobiles BibRef

Yuan, M.X.[Mao-Xun], Wang, Y.Y.[Yin-Yan], Wei, X.X.[Xing-Xing],
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ICIP22(1366-1370)
IEEE DOI 2211
Training, Deep learning, Vehicle detection, Scalability, Semantics, Detectors, Object detection, Deep learning, object detection, vehicle detection BibRef

Zhang, J.C.[Jia-Cheng], Lin, X.R.[Xiang-Ru], Jiang, M.[Minyue], Yu, Y.[Yue], Gong, C.T.[Chen-Ting], Zhang, W.[Wei], Tan, X.[Xiao], Li, Y.Y.[Ying-Ying], Ding, E.[Errui], Li, G.B.[Guan-Bin],
A Multi-granularity Retrieval System for Natural Language-based Vehicle Retrieval,
AICity22(3215-3224)
IEEE DOI 2210
Training, Target tracking, Fuses, Urban areas, Semantics, Force, Linguistics BibRef

Miao, H.[Hui], Lu, F.X.[Fei-Xiang], Liu, Z.D.[Zong-Dai], Zhang, L.J.[Liang-Jun], Manocha, D.[Dinesh], Zhou, B.[Bin],
Robust 2D/3D Vehicle Parsing in Arbitrary Camera Views for CVIS,
ICCV21(15611-15620)
IEEE DOI 2203
Training, Deep learning, Image segmentation, Annotations, Pose estimation, Vision for robotics and autonomous vehicles, Vision applications and systems BibRef

Liu, M.Y.[Meng-Yun], Qi, N.[Na], Shi, Y.H.[Yun-Hui], Yin, B.C.[Bao-Cai],
An Attention Fusion Network for Event-Based Vehicle Object Detection,
ICIP21(3363-3367)
IEEE DOI 2201
Location awareness, Uncertainty, Fuses, Object detection, Predictive models, Feature extraction, vehicle detection, Attention module BibRef

Zhang, H.T.[Hao-Tian], Ji, H.R.[Hao-Rui], Zheng, A.[Aotian], Hwang, J.N.[Jenq-Neng], Hwang, R.H.[Ren-Hung],
Monocular 3D Localization of Vehicles in Road Scenes,
AVVision21(2855-2864)
IEEE DOI 2112
Location awareness, Roads, Cameras, Iron, Sensors, Task analysis BibRef

Sun, P.[Pei], Wang, W.[Weiyue], Chai, Y.N.[Yu-Ning], Elsayed, G.[Gamaleldin], Bewley, A.[Alex], Zhang, X.[Xiao], Sminchisescu, C.[Cristian], Anguelov, D.[Dragomir],
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection,
CVPR21(5721-5730)
IEEE DOI 2111
Measurement, Laser radar, Head, Vehicle detection, Object detection, Detectors BibRef

Koestler, L.[Lukas], Yang, N.[Nan], Wang, R.[Rui], Cremers, D.[Daniel],
Learning Monocular 3D Vehicle Detection Without 3D Bounding Box Labels,
GCPR20(116-129).
Springer DOI 2110
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Hofstetter, I.[Isabell], Springer, M.[Malte], Ries, F.[Florian], Haueis, M.[Martin],
Constellation Codebooks for Reliable Vehicle Localization,
GCPR20(303-315).
Springer DOI 2110
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Bai, S.[Shuai], Zheng, Z.D.[Zhe-Dong], Wang, X.H.[Xiao-Han], Lin, J.Y.[Jun-Yang], Zhang, Z.[Zhu], Zhou, C.[Chang], Yang, H.X.[Hong-Xia], Yang, Y.[Yi],
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval,
AICity21(4029-4038)
IEEE DOI 2109
Code, Vehicle Detection.
WWW Link. Training, Smart cities, Search problems, Robustness, Pattern recognition, Task analysis BibRef

Lee, S.[Sangrok], Woo, T.[Taekang], Lee, S.H.[Sang Hun],
SBNet: Segmentation-based Network for Natural Language-based Vehicle Search,
AICity21(4049-4055)
IEEE DOI 2109
Deep learning, Image segmentation, Law enforcement, Urban areas, Natural languages BibRef

Sun, Z.[Ziruo], Liu, X.F.[Xin-Fang], Bi, X.P.[Xiao-Peng], Nie, X.S.[Xiu-Shan], Yin, Y.L.[Yi-Long],
DUN: Dual-path Temporal Matching Network for Natural Language-based Vehicle Retrieval,
AICity21(4056-4062)
IEEE DOI 2109
Visualization, Silver, Databases, Natural languages, Urban areas, Feature extraction BibRef

Khorramshahi, P.[Pirazh], Rambhatla, S.S.[Sai Saketh], Chellappa, R.[Rama],
Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems,
AICity21(4178-4187)
IEEE DOI 2109
Training, Visualization, Adaptation models, Urban areas, Cameras BibRef

Park, E.J.[Eun-Ju], Kim, H.[Hoyoung], Jeong, S.[Seonghwan], Kang, B.[Byungkon], Kwon, Y.[YoungMin],
Keyword-based Vehicle Retrieval,
AICity21(4215-4222)
IEEE DOI 2109
Training, Deep learning, Urban areas, Semantics, Streaming media, Feature extraction, Particle measurements BibRef

Nguyen, T.M.[Tam Minh], Pham, Q.H.[Quang Huu], Doan, L.B.[Linh Bao], Trinh, H.V.[Hoang Viet], Nguyen, V.A.[Viet-Anh], Phan, V.H.[Viet-Hoang],
Contrastive Learning for Natural Language-Based Vehicle Retrieval,
AICity21(4240-4247)
IEEE DOI 2109
Target tracking, Urban areas, Natural languages, Pattern recognition BibRef

Scribano, C.[Carmelo], Sapienza, D.[Davide], Franchini, G.[Giorgia], Verucchi, M.[Micaela], Bertogna, M.[Marko],
All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers,
AICity21(4248-4257)
IEEE DOI 2109
Training, Visualization, Target tracking, Natural languages, Urban areas, Loss measurement BibRef

Lee, C.[Chaehyeon], Seo, J.[Junghoon], Jung, H.[Heechul],
Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner,
EarthVision21(1172-1181)
IEEE DOI 2109
Training, Vehicle detection, Object detection, Detectors, Benchmark testing BibRef

Azimi, S.M.[Seyed Majid], Bahmanyar, R.[Reza], Henry, C.[Corentin], Kurz, F.[Franz],
EAGLE: Large-Scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery,
ICPR21(6920-6927)
IEEE DOI 2105
Photography, Vehicle detection, Urban areas, Superresolution, Object detection, Tools, Cameras BibRef

Wang, X., Hu, X., Chen, C., Fan, Z., Peng, S.,
Illuminating Vehicles With Motion Priors For Surveillance Vehicle Detection,
ICIP20(2021-2025)
IEEE DOI 2011
Detectors, Surveillance, Feature extraction, Videos, Training, Object detection, Roads, Motion priors, vehicle detection, traffic surveillance videos BibRef

Woo, S., Hwang, S., Kim, W., Lee, J., Lee, D., Lee, S.,
False Positive Removal for 3D Vehicle Detection With Penetrated Point Classifier,
ICIP20(2721-2725)
IEEE DOI 2011
Laser radar, Solid modeling, Automobiles, Shape, Autonomous vehicles, autonomous driving BibRef

Peri, N., Khorramshahi, P., Rambhatla, S.S., Shenoy, V., Rawat, S., Chen, J., Chellappa, R.,
Towards Real-Time Systems for Vehicle Re-Identification, Multi-Camera Tracking, and Anomaly Detection,
City20(2648-2657)
IEEE DOI 2008
Cameras, Task analysis, Training, Feature extraction, Anomaly detection, Robustness, Computational modeling BibRef

Qian, Y.J.[Yi-Jun], Yu, L.J.[Li-Jun], Liu, W.H.[Wen-He], Hauptmann, A.G.[Alexander G.],
ELECTRICITY: An Efficient Multi-camera Vehicle Tracking System for Intelligent City,
City20(2511-2519)
IEEE DOI 2008
Cameras, Feature extraction, Urban areas, Target tracking, Task analysis, Object detection, Computational modeling BibRef

Yu, L.J.[Li-Jun], Qian, Y.J.[Yi-Jun], Liu, W.H.[Wen-He], Hauptmann, A.G.[Alexander G.],
Argus++: Robust Real-time Activity Detection for Unconstrained Video Streams with Overlapping Cube Proposals,
Activity22(112-121)
IEEE DOI 2202
Surveillance, Roads, Streaming media, NIST, Benchmark testing, Real-time systems BibRef

Yu, L.J.[Li-Jun], Feng, Q.Y.[Qian-Yu], Qian, Y.J.[Yi-Jun], Liu, W.H.[Wen-He], Hauptmann, A.G.[Alexander G.],
Zero-VIRUS*: Zero-shot Vehicle Route Understanding System for Intelligent Transportation,
City20(2534-2543)
IEEE DOI 2008
Cameras, Target tracking, Trajectory, Videos, Automobiles, Task analysis BibRef

Liu, Z., Lian, T., Farrell, J., Wandell, B.,
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network,
ADW19(2383-2392)
IEEE DOI 2004
cameras, convolutional neural nets, image processing, object detection, photography, software simulations, metrics BibRef

Yan, X., Yu, Y., Wang, F., Liu, W., He, S., Pan, J.,
Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery,
ICCV19(7617-7626)
IEEE DOI 2004
hidden feature removal, image segmentation, object tracking, traffic engineering computing, video signal processing, Training BibRef

Fattal, A.K.[Ann-Katrin], Karg, M.[Michelle], Scharfenberger, C.[Christian], Adamy, J.[Jürgen],
Distant Vehicle Detection: How Well Can Region Proposal Networks Cope with Tiny Objects at Low Resolution?,
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Springer DOI 1905
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3D Bounding Boxes for Road Vehicles: A One-Stage, Localization Prioritized Approach Using Single Monocular Images,
AutoNUE18(V:626-641).
Springer DOI 1905
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Sandhu, M.[Mahtab], Upadhyay, S.[Sarthak], Krishna, M.[Madhava], Medasani, S.[Shanti],
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AutoNUE18(V:676-687).
Springer DOI 1905
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Sun, Y., Li, M., Lu, J.,
Part-based Multi-stream Model for Vehicle Searching,
ICPR18(1372-1377)
IEEE DOI 1812
Streaming media, Training, Task analysis, Measurement, Feature extraction, Computational modeling, Multi-stream CNN BibRef

Sheeny, M., Wallace, A., Emambakhsh, M., Wang, S., Connor, B.,
POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors,
PBVS18(1328-13286)
IEEE DOI 1812
Convolution, Feature extraction, Neural networks, Meteorology, Sensors, Object detection, Training BibRef

Tokuda, E.K., Ferreira, G.B.A., Silva, C., Cesar, R.M.,
A Novel Semi-Supervised Detection Approach with Weak Annotation,
Southwest18(129-132)
IEEE DOI 1809
Detectors, Quality control, Training, Automobiles, Cameras, Meteorology, Videos BibRef

Fan, W., Ainouz, S., Meriaudeau, F., Bensrhair, A.,
Polarization-Based Car Detection,
ICIP18(3069-3073)
IEEE DOI 1809
Feature extraction, Automobiles, Image color analysis, Color, Roads, Computational modeling, Detectors, Car detection, polarization, road scenes BibRef

Wang, X., Cheng, P., Liu, X., Uzochukwu, B.,
Focal loss dense detector for vehicle surveillance,
ISCV18(1-5)
IEEE DOI 1807
convolution, feedforward neural nets, learning (artificial intelligence), object detection, Vehicle detection BibRef

Moate, C.P.[Chris P.], Hayward, S.D.[Stephen D.], Ellis, J.S.[Jonathan S.], Russell, L.[Lee], Timmerman, R.O.[Ralph O.], Lane, R.O.[Richard O.], Strain, T.J.[Thomas J.],
Vehicle Detection in Infrared Imagery Using Neural Networks with Synthetic Training Data,
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Springer DOI 1807
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Yang, B.[Biao], Zhang, Y.Y.[Yu-Yu], Cao, J.M.[Jin-Meng], Zou, L.[Ling],
On Road Vehicle Detection Using an Improved Faster RCNN Framework with Small-Size Region Up-Scaling Strategy,
PSIVTWS17(241-253).
Springer DOI 1806
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Rujikietgumjorn, S., Watcharapinchai, N.,
Vehicle detection with sub-class training using R-CNN for the UA-DETRAC benchmark,
AVSS17(1-5)
IEEE DOI 1806
feature extraction, learning (artificial intelligence), object detection, road vehicles, traffic engineering computing, Vehicle detection BibRef

Yuan, X.[Xue], Su, S.A.[Shu-Ai], Chen, H.J.[Hou-Jin],
A Graph-Based Vehicle Proposal Location and Detection Algorithm,
ITS(18), No. 12, December 2017, pp. 3282-3289.
IEEE DOI 1712
Cameras, Image color analysis, Image segmentation, Proposals, Sensors, Shape, Vehicle detection, graph based, vehicle proposal location BibRef

Espinosa, J.E.[Jorge E.], Velastin, S.A.[Sergio A.], Branch, J.W.[John W.],
Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study,
IVIC17(3-15).
Springer DOI 1711
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Castrejón, L.[Lluís], Kundu, K.[Kaustav], Urtasun, R.[Raquel], Fidler, S.[Sanja],
Annotating Object Instances with a Polygon-RNN,
CVPR17(4485-4493)
IEEE DOI 1711
Award, CVPR, HM. Agriculture, Image segmentation, Kernel, Labeling. Interactive segmentation of objects. Annotation. Cityscapes, Cars. BibRef

Lee, J.T., Chung, Y.,
Deep Learning-Based Vehicle Classification Using an Ensemble of Local Expert and Global Networks,
Traffic17(920-925)
IEEE DOI 1709
Automobiles, Bicycles, Error analysis, Lighting, Training, Training, data BibRef

Liu, J.X.[Ji-Xin], Sun, N.[Ning], Han, G.[Guang], Yang, H.G.[Hai-Gen],
Vehicle sparse recognition via class dictionary learning,
ICIVC17(185-188)
IEEE DOI 1708
Automobiles, Bicycles, Databases, Dictionaries, Motorcycles, Training, dictionary learning, intelligent transportation, sparse framework, vehicle recognition BibRef

Farhat, M., Mhiri, S., Tagina, M.,
Free training object detection based on multi-stage fusion using belief functions,
ISIVC16(153-158)
IEEE DOI 1704
Automobiles BibRef

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Instance-Level Segmentation of Vehicles by Deep Contours,
CVTSV16(I: 477-492).
Springer DOI 1704
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Møgelmose, A., Moeslund, T.B.,
Analyzing Wheels of Vehicles in Motion Using Laser Scanning,
Traffic16(1601-1608)
IEEE DOI 1612
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Orthogonal Gradient-Based Binary Image Representation for Vehicle Detection,
ICCVG16(453-461).
Springer DOI 1611
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Zhou, Y.[Yi], Liu, L.[Li], Shao, L.[Ling], Mellor, M.[Matt],
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation,
ECCV16(II: 278-293).
Springer DOI 1611
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Fang, Y., Sun, L., Fu, H., Wu, T., Wang, R., Dai, B.,
Learning deep compact channel features for object detection in traffic scenes,
ICIP16(1052-1056)
IEEE DOI 1610
Benchmark testing BibRef

Huang, J., You, S.,
Vehicle detection in urban point clouds with orthogonal-view convolutional neural network,
ICIP16(2593-2597)
IEEE DOI 1610
Automobiles BibRef

Xu, H., Huang, Q., Kuo, C.C.J.,
Car detection using deformable part models with composite features,
ICIP16(3812-3816)
IEEE DOI 1610
Automobiles BibRef

Sedaghat, N.[Nima], Brox, T.[Thomas],
Unsupervised Generation of a View Point Annotated Car Dataset from Videos,
ICCV15(1314-1322)
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Struwe, M.[Marvin], Hasler, S.[Stephan], Bauer-Wersing, U.[Ute],
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Vehicles Detection in Stereo Vision Based on Disparity Map Segmentation and Objects Classification,
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ARGOS: Venice Boat Classification,
AVSS15(1-6)
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boats BibRef

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Vehicle identification using distance-based appearance model,
AVSS15(1-4)
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Calibration BibRef

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CVPR15(3973-3981)
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feature extraction BibRef

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DICTA14(1-7)
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image matching. Find similar layouts to images. BibRef

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ICIP14(1609-1613)
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Cameras; Detectors; Lighting; Standards; Surveillance; Training; Vehicles BibRef

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Vehicle detection BibRef

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IEEE DOI 1208
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Lv, Y.[Yang], Yao, B.[Benjamin], Wang, Y.T.[Yong-Tian], Zhu, S.C.[Song-Chun],
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Springer DOI 1109
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VS10(246-255).
Springer DOI 1109
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WACV11(599-605).
IEEE DOI 1101
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Vehicle Detection and Roadside Tree Shadow Removal in High Resolution Satellite Images,
GEOBIA10(xx-yy).
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ICIP10(4649-4652).
IEEE DOI 1009
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Audio-Visual Co-Training for Vehicle Classification,
AVSS10(586-592).
IEEE DOI 1009
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IEEE DOI 1008
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A Real Time Vehicle Detection Algorithm for Vision-Based Sensors,
ICCVG10(II: 211-218).
Springer DOI 1009
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IEEE DOI 1004
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CISP09(1-5).
IEEE DOI 0910
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ACCV10(III: 262-275).
Springer DOI 1011
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CVPR09(2703-2710).
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CIMSVP09(35-40).
IEEE DOI 0903
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Zhang, Z.X.[Zhao-Xiang], Li, M.[Min], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
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ICPR08(1-4).
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ICPR08(1-4).
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See also Robust automated ground plane rectification based on moving vehicles for traffic scene surveillance. BibRef

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SSC08(27-40).
Springer DOI 0810
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ICIP08(2384-2387).
IEEE DOI 0810
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Vehicle Classification on Multi-Sensor Smart Cameras Using Feature- and Decision-Fusion,
ICDSC07(67-74).
IEEE DOI 0709
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Rojo Ruiz, A.[Arturo], Sánchez Fernandez, L.P.[Luis P.], Felipe-Riverón, E.[Edgardo], Suárez Guerra, S.[Sergio],
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CIARP08(14-21).
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Gao, L.[Lei], Li, C.[Chao], Fang, T.[Ting], Xiong, Z.[Zhang],
Vehicle Detection Based on Color and Edge Information,
ICIAR08(xx-yy).
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Alonso, D.[Daniel], Salgado, L.[Luis], Nieto, M.[Marcos],
Robust Vehicle Detection Through Multidimensional Classification for on Board Video Based Systems,
ICIP07(IV: 321-324).
IEEE DOI 0709
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Katahara, S.J.[Shun-Ji], Aoki, M.[Masayoshi],
Vehicle Detection Using Double Slit Camera,
ACCV06(II:162-170).
Springer DOI 0601
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Fang, J.Z.[Jian-Zhong], Qiu, G.P.[Guo-Ping],
Car/Non-Car Classification in an Informative Sample Subspace,
ICPR06(II: 962-965).
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A Sequential Vehicle Classifier for Infrared Video using Multinomial Pattern Matching,
OTCBVS06(127).
IEEE DOI 0609
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Lee, D.[Deaho], Park, Y.T.[Young-Tae],
Robust vehicle detection based on shadow classification,
ICPR06(III: 1167-1170).
IEEE DOI 0609
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Cheng, H.[Hong], Zheng, N.N.[Nan-Ning], Sun, C.[Chong],
Boosted Gabor Features Applied to Vehicle Detection,
ICPR06(I: 662-666).
IEEE DOI 0609
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Gronwall, C., Anderson, P., Gustafsson, F.[Fredrik],
Least Squares Fitting Articulated Objects,
SafeSecur05(III: 116-116).
IEEE DOI 0507
Vehicles. BibRef

Nowak, E., Jurie, F.,
Vehicle Categorization: Parts for Speed and Accuracy,
PETS05(277-283).
IEEE DOI 0602
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Wang, Y.K.[Yuan-Kai], Chen, S.H.[Shao-Hua],
Robust vehicle detection approach,
AVSBS05(117-122).
IEEE DOI 0602
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Edge-Based Rich Representation for Vehicle Classification,
ICCV05(II: 1185-1192).
IEEE DOI 0510
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Hilario, C.[Cristina], Collado, J.M.[Juan Manuel], Armingol, J.M.[José Maria], de la Escalera, A.[Arturo],
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IbPRIA05(I:579).
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Vehicle Area Segmentation Using Grid-Based Feature Values,
CAIP05(464).
Springer DOI 0509
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Yalcin, H.[Hulya], Hebert, M.[Martial], Collins, R.T.[Robert T.], Black, M.J.[Michael J.],
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Izri, S.[Sonia], Brassart, E.[Eric], Delahoche, L.[Laurent], Marhic, B.[Bruno], Clérentin, A.[Arnaud],
Detection of Vehicles in a Motorway Environment by Means of Telemetric and Visual Data,
ICIAR04(II: 471-480).
Springer DOI 0409
BibRef

Zhao, Y.N.[Ying-Nan], Yang, J.Y.[Jing-Yu],
Weighted features for infrared vehicle verification based on Gabor filters,
ICARCV04(I: 671-675).
IEEE DOI 0412
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Kurata, R., Watanabe, H., Tohno, M., Ishii, T., Oouchi, H.,
Evaluation of the detection characteristics of road sensors under poor-visibility conditions,
IVS04(538-543).
IEEE DOI 0411
Detect vehicles. BibRef

Hirahara, K., Ikeuchi, K.,
Extraction of vehicle image from panoramic street-image,
IVS04(756-761).
IEEE DOI 0411
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Chu, J.W.[Jiang-Wei], Jin, L.S.[Li-Sheng], Guo, L.[Lie], Libibing, Wang, R.B.[Rong-Ben],
Study on method of detecting preceding vehicle based on monocular camera,
IVS04(750-755).
IEEE DOI 0411
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Funck, S., Mohler, N., Oertel, W.,
Determining car-park occupancy from single images,
IVS04(325-328).
IEEE DOI 0411
BibRef

Broggi, A., Cerri, P., Antonello, P.C.,
Multi-resolution vehicle detection using artificial vision,
IVS04(310-314).
IEEE DOI 0411
BibRef

Hoffman, C., Dang, T., Stiller, C.,
Vehicle detection fusing 2D visual features,
IVS04(280-285).
IEEE DOI 0411
BibRef

Takizawa, H., Yamada, K., Ito, T.,
Vehicles detection using sensor fusion,
IVS04(238-243).
IEEE DOI 0411
BibRef

Huang, C.L.[Chung-Lin], Liao, W.C.[Wen-Chieh],
A vision-based vehicle identification system,
ICPR04(IV: 364-367).
IEEE DOI 0409
BibRef

Zhu, Z.F.[Zhen-Feng], Zhao, Y.[Yao], Lu, H.Q.[Han-Qing],
Sequential Architecture for Efficient Car Detection,
VS07(1-8).
IEEE DOI 0706
BibRef

Yang, H., Lou, J., Sun, H., Hu, W., Tan, T.,
Efficient and Robust Vehicle Localization,
ICIP01(II: 355-358).
IEEE DOI 0108
BibRef

Cucchiara, R., Piccardi, M., Prati, A., Scarabottolo, N.,
Real-time detection of moving vehicles,
CIAP99(618-623).
IEEE DOI 9909
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Nighttime Vehicle Detection and Recognition .


Last update:Mar 16, 2024 at 20:36:19