16.7.2.1.1 Vehicle Make or Model or Type Recogniton

Chapter Contents (Back)
Vehicle Recognition. Vehicle Detection. Fine-Grained Vehicle. Includes drone detection.

Sullivan, G.D., Baker, K.D., Anderson, J.A.D.W.,
Use of Multiple Difference-of-Gaussian Filters to Verify Geometric Models,
IVC(3), No. 4, November 1985, pp. 192-197.
Elsevier DOI London buses in street scenes. 3D model and camera and scene parameters. BibRef 8511

Ghosh, N.[Nirmalya], Bhanu, B.[Bir],
Incremental Unsupervised Three-Dimensional Vehicle Model Learning From Video,
ITS(11), No. 2, June 2010, pp. 423-440.
IEEE DOI 1007
BibRef
Earlier:
How current BNs fail to represent evolvable pattern recognition problems and a proposed solution,
ICPR08(1-4).
IEEE DOI 0812
BibRef
Earlier:
Bayesian based 3D shape reconstruction from video,
ICIP08(1152-1155).
IEEE DOI 0810
BibRef
Earlier:
Incremental Vehicle 3-D Modeling from Video,
ICPR06(III: 272-275).
IEEE DOI 0609
BibRef

Zhang, B.,
Reliable Classification of Vehicle Types Based on Cascade Classifier Ensembles,
ITS(14), No. 1, March 2013, pp. 322-332.
IEEE DOI 1303
BibRef

Ghosh, N.[Nirmalya], Bhanu, B.[Bir],
Evolving Bayesian Graph for Three-Dimensional Vehicle Model Building From Video,
ITS(15), No. 2, April 2014, pp. 563-578.
IEEE DOI 1404
Buildings BibRef

Thakoor, N.S.[Ninad S.], Bhanu, B.[Bir],
Structural Signatures for Passenger Vehicle Classification in Video,
ITS(14), No. 4, 2013, pp. 1796-1805.
IEEE DOI 1312
BibRef
Earlier: ICPR12(926-929).
WWW Link. 1302
Feature extraction BibRef

Thakoor, N.S.[Ninad S.], Bhanu, B.[Bir],
Efficient alignment for vehicle make and model recognition,
ICIP14(5542-5546)
IEEE DOI 1502
Accuracy BibRef

Zhang, B.L.[Bai-Ling], Zhao, C.H.[Chi-Hang], He, J.[Jie],
Classification of vehicle type and make by combined features and random subspace ensemble,
IJCVR(3), No. 1-2, 2012, pp. 35-51.
DOI Link 1204
BibRef
Earlier: A1, A2, Only:
Classification of Vehicle Make by Combined Features and Random Subspace Ensemble,
ICIG11(920-925).
IEEE DOI 1109
BibRef

Zhang, B.L.[Bai-Ling],
Classification and identification of vehicle type and make by cortex-like image descriptor HMAX,
IJCVR(4), No. 3, 2014, pp. 195-211.
DOI Link 1407
BibRef

Zhang, B.L.[Bai-Ling], Zhou, Y.F.[Yi-Fan], Pan, H.[Hao], Tillo, T.[Tammam],
Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification,
MVA(25), No. 2, February 2014, pp. 437-450.
WWW Link. 1402
BibRef

Chen, Z.Z.[Ze-Zhi], Pears, N., Freeman, M., Austin, J.,
A Gaussian mixture model and support vector machine approach to vehicle type and colour classification,
IET-ITS(8), No. 2, March 2014, pp. 135-144.
DOI Link 1406
Gaussian processes BibRef

Hsieh, J.W.[Jun-Wei], Chen, L.C.[Li-Chih], Chen, D.Y.[Duan-Yu],
Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition,
ITS(15), No. 1, February 2014, pp. 6-20.
IEEE DOI 1403
feature extraction BibRef

Chen, Z.Z.[Ze-Zhi], Ellis, T.,
Semi-automatic annotation samples for vehicle type classification in urban environments,
IET-ITS(9), No. 3, 2015, pp. 240-249.
DOI Link 1506
closed circuit television BibRef

Dong, Z.[Zhen], Wu, Y., Pei, M.T.[Ming-Tao], Jia, Y.D.[Yun-De],
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network,
ITS(16), No. 4, August 2015, pp. 2247-2256.
IEEE DOI 1508
Convolution BibRef

Dong, Z.[Zhen], Pei, M.T.[Ming-Tao], He, Y.[Yang], Liu, T.[Ting], Dong, Y.M.[Yan-Mei], Jia, Y.D.[Yun-De],
Vehicle Type Classification Using Unsupervised Convolutional Neural Network,
ICPR14(172-177)
IEEE DOI 1412
Accuracy BibRef

Chen, L.C.[Li-Chih], Hsieh, J.W.[Jun-Wei], Yan, Y.[Yilin], Chen, D.Y.[Duan-Yu],
Vehicle make and model recognition using sparse representation and symmetrical SURFs,
PR(48), No. 6, 2015, pp. 1979-1998.
Elsevier DOI 1503
Symmetrical SURF BibRef

Hsieh, J.W.[Jun-Wei], Chen, L.C.[Li-Chih], Chen, D.Y.[Duan-Yu], Cheng, S.C.[Shyi-Chyi],
Vehicle make and model recognition using symmetrical SURF,
AVSS13(472-477)
IEEE DOI 1311
computer vision BibRef

He, H., Shao, Z., Tan, J.,
Recognition of Car Makes and Models From a Single Traffic-Camera Image,
ITS(16), No. 6, December 2015, pp. 3182-3192.
IEEE DOI 1512
Cameras BibRef

Siddiqui, A.J.[Abdul Jabbar], Mammeri, A.[Abdelhamid], Boukerche, A.[Azzedine],
Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features,
ITS(17), No. 11, November 2016, pp. 3205-3219.
IEEE DOI 1609
Dictionaries BibRef

Boukerche, A.[Azzedine], Siddiqui, A.J.[Abdul Jabbar], Mammeri, A.[Abdelhamid],
Automated Vehicle Detection and Classification: Models, Methods, and Techniques,
Surveys(50), No. 5, November 2017, pp. Article No 62.
DOI Link 1712
Survey, Vehicle Detection. Categorize based on granularity of recognition. BibRef

Bitar, N., Refai, H.H.,
A Probabilistic Approach to Improve the Accuracy of Axle-Based Automatic Vehicle Classifiers,
ITS(18), No. 3, March 2017, pp. 537-544.
IEEE DOI 1703
Axles BibRef

Fang, J., Zhou, Y., Yu, Y., Du, S.,
Fine-Grained Vehicle Model Recognition Using A Coarse-to-Fine Convolutional Neural Network Architecture,
ITS(18), No. 7, July 2017, pp. 1782-1792.
IEEE DOI 1706
Feature extraction, Image recognition, Solid modeling, Support vector machines, Training, Vehicles, Fine-grained vehicle recognition, convolutional neural network, one-versus-all, SVM BibRef

Biglari, M.[Mohsen], Soleimani, A.[Ali], Hassanpour, H.[Hamid],
Part-based recognition of vehicle make and model,
IET-IPR(11), No. 7, July 2017, pp. 483-491.
DOI Link 1707
BibRef

Chen, L.[Long], He, Y.H.[Yu-Hang], Fan, L.[Lei],
Let the robot tell: Describe car image with natural language via LSTM,
PRL(98), No. 1, 2017, pp. 75-82.
Elsevier DOI 1710
BibRef

Hu, Q., Wang, H., Li, T., Shen, C.,
Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition,
ITS(18), No. 11, November 2017, pp. 3147-3156.
IEEE DOI 1711
Automobiles, Cameras, Feature extraction, Robustness, Surveillance, car model classification. BibRef

Biglari, M., Soleimani, A., Hassanpour, H.,
A Cascaded Part-Based System for Fine-Grained Vehicle Classification,
ITS(19), No. 1, January 2018, pp. 273-283.
IEEE DOI 1801
Automobiles, Data mining, Deformable models, Feature extraction, Support vector machines, Training, Fine-grained classification, vehicle make and model recognition BibRef

Gao, Y.B.[Yong-Bin], Lee, H.J.[Hyo Jong],
Car manufacturer and model recognition based on scale invariant feature transform,
IJCVR(8), No. 1, 2018, pp. 32-41.
DOI Link 1804
BibRef

Yang, D.[Dan], Qian, Y.L.[Yan-Lin], Chen, K.[Ke], Berki, E.[Eleni], Kämäräinen, J.K.[Joni-Kristian],
Hierarchical Sliding Slice Regression for Vehicle Viewing Angle Estimation,
ITS(19), No. 6, June 2018, pp. 2035-2042.
IEEE DOI 1806
Automobiles, Correlation, Estimation, Manifolds, Robustness, Space vehicles, Visualization, Visual regression, viewing angle estimation BibRef

Luo, Z., Branchaud-Charron, F., Lemaire, C., Konrad, J., Li, S., Mishra, A., Achkar, A., Eichel, J., Jodoin, P.,
MIO-TCD: A New Benchmark Dataset for Vehicle Classification and Localization,
IP(27), No. 10, October 2018, pp. 5129-5141.
IEEE DOI 1808
cameras, image classification, learning (artificial intelligence), object detection, vehicle classification BibRef


Sommer, L., Schumann, A., Müller, T., Schuchert, T., Beyerer, J.,
Flying object detection for automatic UAV recognition,
AVSS17(1-6)
IEEE DOI 1806
autonomous aerial vehicles, image recognition, neurocontrollers, object detection, robot vision, video cameras, Unmanned aerial vehicles BibRef

Schumann, A., Sommer, L., Klatte, J., Schuchert, T., Beyerer, J.,
Deep cross-domain flying object classification for robust UAV detection,
AVSS17(1-6)
IEEE DOI 1806
image classification, image sequences, learning (artificial intelligence), mobile robots, neural nets, Robustness BibRef

Unlu, E., Zenou, E., Riviere, N.,
Ordered minimum distance bag-of-words approach for aerial object identification,
AVSS17(1-6)
IEEE DOI 1806
computer vision, feature extraction, object detection, object recognition, SURF based object recognition, Visualization BibRef

Aker, C., Kalkan, S.,
Using deep networks for drone detection,
AVSS17(1-6)
IEEE DOI 1806
autonomous aerial vehicles, convolution, image sequences, neural nets, object detection, video signal processing, Training BibRef

Saqib, M., Daud Khan, S., Sharma, N., Blumenstein, M.,
A study on detecting drones using deep convolutional neural networks,
AVSS17(1-5)
IEEE DOI 1806
autonomous aerial vehicles, computer vision, convolution, learning (artificial intelligence), neural nets, Training BibRef

Selbes, B., Sert, M.,
Multimodal vehicle type classification using convolutional neural network and statistical representations of MFCC,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, feature selection, image classification, image fusion, image representation, Visualization BibRef

Ma, C., Liu, D., Peng, X., Wu, F.,
Surveillance video coding with vehicle library,
ICIP17(270-274)
IEEE DOI 1803
Cameras, Encoding, Feature extraction, Indexes, Libraries, Surveillance, Video coding, HEVC, Inter prediction, Vehicle library BibRef

Yan, K., Tian, Y., Wang, Y., Zeng, W., Huang, T.,
Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles,
ICCV17(562-570)
IEEE DOI 1802
computer vision, image classification, learning (artificial intelligence), pose estimation, probability, Visualization Dataset: See also PKU-VD Dataset. BibRef

Liu, H., Tian, Y., Wang, Y., Pang, L., Huang, T.,
Deep Relative Distance Learning: Tell the Difference between Similar Vehicles,
CVPR16(2167-2175)
IEEE DOI 1612
Dataset: See also PKU VehicleID Dataset. BibRef

Zwemer, M.H., Brouwers, G.M.Y.E., Wijnhoven, R.G.J.[Rob G.J.], de With, P.H.N.[Peter H.N.],
Semi-automatic Training of a Vehicle Make and Model Recognition System,
CIAP17(II:321-332).
Springer DOI 1711
BibRef

Jung, H., Choi, M.K., Jung, J., Lee, J.H., Kwon, S., Jung, W.Y.,
ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems,
Traffic17(934-940)
IEEE DOI 1709
Computer vision, Conferences, Feature extraction, Pattern recognition, Proposals BibRef

Kim, P.K., Lim, K.T.,
Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image,
Traffic17(914-919)
IEEE DOI 1709
Automobiles, Bagging, Error analysis, Machine learning, Pattern recognition, Surveillance, Training BibRef

Theagarajan, R., Pala, F., Bhanu, B.,
EDeN: Ensemble of Deep Networks for Vehicle Classification,
Traffic17(906-913)
IEEE DOI 1709
Automobiles, Cameras, Computer architecture, Radar tracking, Surveillance, Traffic control, Training BibRef

Tafazzoli, F., Frigui, H., Nishiyama, K.,
A Large and Diverse Dataset for Improved Vehicle Make and Model Recognition,
Traffic17(874-881)
IEEE DOI 1709
Automobiles, Cameras, Computational modeling, Licenses, Robustness, Surveillance, Three-dimensional, displays BibRef

Ma, K.[Kaili], Zhang, J.[Jun], Wang, F.L.[Feng-Lei], Tu, D.[Dan], Li, S.H.[Shuo-Hao],
Fine-grained object detection based on self-adaptive anchors,
ICIVC17(78-82)
IEEE DOI 1708
Approximation algorithms, Automobiles, Clustering algorithms, Feature extraction, Object detection, Proposals, Training, convolutional neural network, faster R-CNN, fine-grained object, self-adaptive, anchors BibRef

Tafazzoli, F., Frigui, H.,
Vehicle make and model recognition using local features and logo detection,
ISIVC16(353-358)
IEEE DOI 1704
Detectors BibRef

Li, B.[Bo], Wu, T.F.[Tian-Fu], Xiong, C.M.[Cai-Ming], Zhu, S.C.[Song-Chun],
Recognizing Car Fluents from Video,
CVPR16(3803-3812)
IEEE DOI 1612
Time varying states in dynamic scenes. Details. BibRef

Sochor, J.[Jakub], Herout, A.[Adam], Havel, J.,
BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition,
CVPR16(3006-3015)
IEEE DOI 1612
BibRef

Boyle, J.[Jonathan], Ferryman, J.M.[James M.],
Vehicle subtype, make and model classification from side profile video,
AVSS15(1-6)
IEEE DOI 1511
Cameras BibRef

Fraz, M.[Muhammad], Edirisinghe, E.A.[Eran A.], Sarfraz, M.S.[M. Saquib],
Mid-level-Representation Based Lexicon for Vehicle Make and Model Recognition,
ICPR14(393-398)
IEEE DOI 1412
Computational modeling BibRef

Dong, Z.[Zhen], Jia, Y.D.[Yun-De],
Vehicle type classification using distributions of structural and appearance-based features,
ICIP13(4321-4324)
IEEE DOI 1402
Vehicle type classification BibRef

Abdel Maseeh, M.[Meena], Badreldin, I.[Islam], Abdelkader, M.F.[Mohamed F.], El Saban, M.[Motaz],
Car Make and Model recognition combining global and local cues,
ICPR12(910-913).
WWW Link. 1302
BibRef

Pearce, G., Pears, N.E.,
Automatic make and model recognition from frontal images of cars,
AVSBS11(373-378).
IEEE DOI 1111
BibRef

Negri, P.[Pablo], Clady, X.[Xavier], Milgram, M.[Maurice], Poulenard, R.[Raphael],
An Oriented-Contour Point Based Voting Algorithm for Vehicle Type Classification,
ICPR06(I: 574-577).
IEEE DOI 0609
BibRef

Ozcanli, O.C.[Ozge C.], Tamrakar, A.[Amir], Kimia, B.B.[Benjamin B.],
Augmenting Shape with Appearance in Vehicle Category Recognition,
CVPR06(I: 935-942).
IEEE DOI 0606
BibRef

Petrovic, V.S., Cootes, T.F.,
Analysis of Features for Rigid Structure Vehicle Type Recognition,
BMVC04(xx-yy).
HTML Version. 0508
BibRef
And:
Vehicle type recognition with match refinement,
ICPR04(III: 95-98).
IEEE DOI 0409
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Vehicle Recogniton, Lidar, Laser Data, Depth Data .


Last update:Aug 16, 2018 at 18:22:30