# 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 ATR -- Vehicles, Aerial Images, Vehicle Detection. Primarily related to driving, other vehicles:

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:

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

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).
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

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

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.
BibRef
And:
Fast Vehicle Localization and Recognition without Line Extraction and Matching,
BMVC94(95-104).
PDF File. 9409
(Discrepancy in page number.)

Tan, T.N.,
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

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

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

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.,
VISP(148), No. 1, February 2001, pp. 78-84. 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

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

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

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, Best Paper. Vehicle Detection. Car detection; Aerial image; Adaboost; On-line learning; Pattern recognition; UltraCamD 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

Onkarappa, N.[Naveen], Sappa, A.D.[Angel D.],
A Novel Space Variant Image Representation,
JMIV(47), No. 1-2, September 2013, pp. 48-59.
BibRef
Earlier:
Space Variant Representations for Mobile Platform Vision Applications,
CAIP11(II: 146-154).
Springer DOI 1109
BibRef
And:
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow,
ICIAR10(I: 230-239).
Springer DOI 1006
BibRef

A featureless and stochastic approach to on-board stereo vision system pose,
IVC(27), No. 9, 3 August 2009, pp. 1382-1393.
Elsevier DOI 0906
BibRef
Earlier: A2, A1:
Real-Time Vehicle Ego-Motion Using Stereo Pairs and Particle Filters,
ICIAR07(469-480).
Springer DOI 0708
On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping 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

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
BibRef
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
BibRef
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.

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

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).
user specifies a set of vehicle characteristics (such as color, direction of travel, speed, length, height, etc.) BibRef

Huang, D.Y.[Deng-Yuan], Chen, C.H.[Chao-Ho], Hu, W.C.[Wu-Chih], Su, S.S.[Sing-Syong],
Reliable moving vehicle detection based on the filtering of swinging tree leaves and raindrops,
JVCIR(23), No. 4, May 2012, pp. 648-664.
Elsevier DOI 1205
Traffic surveillance system; Motion detection; Motion estimation; Motion compensation; Background subtraction; Swinging trees filtering; Raindrops filtering; Shadow elimination BibRef

Wu, B.F.[Bing-Fei], Juang, J.H.[Jhy-Hong],
Adaptive Vehicle Detector Approach for Complex Environments,
ITS(13), No. 2, June 2012, pp. 817-827.
IEEE DOI 1206
BibRef

Teoh, S.S.[Soo Siang], Bräunl, T.[Thomas],
Symmetry-based monocular vehicle detection system,
MVA(23), No. 5, September 2012, pp. 831-842.
BibRef

Mithun, N.C., Rashid, N.U., Rahman, S.M.M.,
Detection and Classification of Vehicles From Video Using Multiple Time-Spatial Images,
ITS(13), No. 3, September 2012, pp. 1215-1225.
IEEE DOI 1209
BibRef

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
BibRef

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
BibRef

Liu, L.W.[Li-Wei], Xing, J.L.[Jun-Liang], Duan, G.Q.[Gen-Quan], Ai, H.Z.[Hai-Zhou],
PRL(36), No. 1, 2014, pp. 154-160.
Elsevier DOI 1312
Vehicle detection BibRef

León, L.C.[Leissi Castańeda], 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
BibRef
Earlier: A1, A3, A4, A5, Only:
Coupling-and-Decoupling: A Hierarchical Model for Occlusion-Free Car Detection,
ACCV12(I:164-175).
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.
image 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,
CirSysVideo(25), No. 1, January 2015, pp. 38-50.
IEEE DOI 1502

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.
BibRef

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

Park, J.H.[Jae-Hyuck], Tai, Y.W.[Yu-Wing],
A simulation based method for vehicle motion prediction,
CVIU(136), No. 1, 2015, pp. 79-91.
Elsevier DOI 1506

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
BibRef
Earlier:
Fast and Robust Object Detection Using Visual Subcategories,
IWMV14(179-184)
IEEE DOI 1409
feature extraction. multiview vehicle detection

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
Cameras

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
BibRef

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

Noh, S.[Seung_Jong], Jeon, M.[Moongu],
Vehicle Detection Using Local Size-Specific Classifiers,
IEICE(E99-D), No. 9, September 2016, pp. 2351-2359.
BibRef

Zhuang, X., Kang, W., Wu, Q.,
Real-time vehicle detection with foreground-based cascade classifier,
IET-IPR(10), No. 4, 2016, pp. 289-296.
Haar transforms 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.
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.
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

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

Chen, L., An, D., Huang, X., Zhou, Z.,
A 3D Reconstruction Strategy of Vehicle Outline Based on Single-Pass Single-Polarization CSAR Data,
IP(26), No. 11, November 2017, pp. 5545-5554.
IEEE DOI 1709
Geometry, Image reconstruction, Imaging, Reflection, Scattering, Synthetic aperture radar, 3D reconstruction, circular synthetic aperture radar (CSAR), vehicle, outline 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

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.
BibRef

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
computer vision, 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, Computer vision, 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.
BibRef

Miao, Y.N.[Ya-Nan], Tao, X.M.[Xiao-Ming], Lu, J.H.[Jian-Hua],
Robust Monocular 3D Car Shape Estimation From 2D Landmarks,
CirSysVideo(28), No. 3, March 2018, pp. 652-663.
IEEE DOI 1804
BibRef
Earlier:
Robust 3D Car Shape Estimation from Landmarks in Monocular Image,
BMVC16(xx-yy).
HTML Version. 1805
cameras, convergence of numerical methods, inverse problems, pose estimation, 3D shape, Stiefel manifold, shape estimation BibRef

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.
BibRef

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.
BibRef

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

Wang, Y.[Yong], Ding, L.[Lu], Laganičre, R.[Robert],
Real-Time UAV Tracking Based on PSR Stability,
VisDrone19(144-152)
IEEE DOI 2004
autonomous aerial vehicles, computational complexity, image filtering, image fusion, mobile robots, object detection, correlation filters BibRef

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.
BibRef

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

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

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.
BibRef

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.
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

Bai, S.[Shuai], Zheng, Z.[Zhedong], Wang, X.H.[Xiao-Han], Lin, J.[Junyang], 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, Computer architecture, 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, Computer architecture, 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, Computer architecture, 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

Liu, Z., Lian, T., Farrell, J., Wandell, B.,
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network,
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

Khirodkar, R.[Rawal], Yoo, D.H.[Dong-Hyun], Kitani, K.M.[Kris M.],
Domain Randomization for Scene-Specific Car Detection and Pose Estimation,
WACV19(1932-1940)
IEEE DOI 1904
object detection, pose estimation, real-world data distribution, domain gap, appearance randomization, synthetic objects, Task analysis BibRef

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
computer vision, convolution, feedforward neural nets, learning (artificial intelligence), object detection, Vehicle detection BibRef

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

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, Computer architecture, Image segmentation, Kernel, Labeling. Interactive segmentation of objects. Annotation. Cityscapes, Cars. BibRef

Chabot, F., Chaouch, M., Rabarisoa, J., Teuličre, C., Chateau, T.,
Deep MANTA: A Coarse-to-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis from Monocular Image,
CVPR17(1827-1836)
IEEE DOI 1711
Object detection, Pose estimation, Proposals, Shape, Solid modeling 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

Sedaghat, N.[Nima], Brox, T.[Thomas],
Unsupervised Generation of a View Point Annotated Car Dataset from Videos,
ICCV15(1314-1322)
IEEE DOI 1602
Cameras. Generation of the dataset. BibRef

Zhang, D.H.[Dong-Hao], He, X.M.[Xu-Ming], Li, H.X.[Han-Xi],
Data-Driven Street Scene Layout Estimation for Distant Object Detection,
DICTA14(1-7)
IEEE DOI 1502
image matching. Find similar layouts to images. BibRef

Feris, R.[Rogerio], Brown, L.M.[Lisa M.], Pankanti, S.[Sharath], Sun, M.T.[Ming-Ting],
Appearance-Based Object Detection Under Varying Environmental Conditions,
ICPR14(166-171)
IEEE DOI 1412
Cameras; Detectors; Lighting; Standards; Surveillance; Training; Vehicles BibRef

Li, B.[Bo], Hu, W.Z.[Wen-Ze], Wu, T.F.[Tian-Fu], Zhu, S.C.[Song-Chun],
Modeling Occlusion by Discriminative AND-OR Structures,
ICCV13(2560-2567)
IEEE DOI 1403
AND-OR structure; CAD simulation; Car Detection; Occlusion Modeling BibRef

Yalcin, H.[Hulya], Hebert, M.[Martial], Collins, R.T.[Robert T.], Black, M.J.[Michael J.],
A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video,
CVPR05(II: 1202).
IEEE DOI 0507
BibRef
And: A1, A3, A4, A2: CMU-RI-TR-05-11, March, 2005.