16.7.2.4.1 Vehicle Tracking, Re-Identification

Chapter Contents (Back)
Vehicle Tracking. Vehicle Re-Identification. Re-Identification, Vehicles.

Oh, C., Ritchie, S.G., Jeng, S.T.,
Anonymous Vehicle Reidentification Using Heterogeneous Detection Systems,
ITS(8), No. 3, September 2007, pp. 460-469.
IEEE DOI 0710
BibRef

Oh, C., Tok, Y.C.A., Ritchie, S.G.,
Real-Time Freeway Level of Service Using Inductive-Signature-Based Vehicle Reidentification System,
ITS(6), No. 2, June 2005, pp. 138-146.
IEEE Abstract. 0506
BibRef

Jeng, S.T.[Shin-Ting], Tok, Y.C.A., Ritchie, S.G.,
Freeway Corridor Performance Measurement Based on Vehicle Reidentification,
ITS(11), No. 3, September 2010, pp. 639-646.
IEEE DOI 1003
BibRef

Ndoye, M., Totten, V.F., Krogmeier, J.V., Bullock, D.M.,
Sensing and Signal Processing for Vehicle Reidentification and Travel Time Estimation,
ITS(12), No. 1, March 2011, pp. 119-131.
IEEE DOI 1103
BibRef

Lin, W.H., Tong, D.,
Vehicle Re-Identification With Dynamic Time Windows for Vehicle Passage Time Estimation,
ITS(12), No. 4, December 2011, pp. 1057-1063.
IEEE DOI 1112
BibRef

Zhou, Y., Liu, L., Shao, L.,
Vehicle Re-Identification by Deep Hidden Multi-View Inference,
IP(27), No. 7, July 2018, pp. 3275-3287.
IEEE DOI 1805
automobiles, computer vision, convolution, feedforward neural nets, image representation, inference mechanisms, spatially concatenated ConvNet BibRef

Zhou, Y., Shao, L.,
Viewpoint-Aware Attentive Multi-view Inference for Vehicle Re-identification,
CVPR18(6489-6498)
IEEE DOI 1812
BibRef
Earlier:
Vehicle Re-Identification by Adversarial Bi-Directional LSTM Network,
WACV18(653-662)
IEEE DOI 1806
Feature extraction, Visualization, Training, Extraterrestrial measurements, Image color analysis, Task analysis. image representation, intelligent transportation systems, object detection, Visualization BibRef

Liu, X.C.[Xin-Chen], Liu, W.[Wu], Mei, T.[Tao], Ma, H.D.[Hua-Dong],
PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance,
MultMed(20), No. 3, March 2018, pp. 645-658.
IEEE DOI 1802
BibRef
Earlier:
A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance,
ECCV16(II: 869-884).
Springer DOI 1611
Cameras, Image color analysis, Licenses, Multimedia communication, Spatiotemporal phenomena, Video surveillance, Progressive search, vehicle re-identification BibRef

Bashir, R.M.S.[Raja Muhammad Saad], Shahzad, M.[Muhammad], Fraz, M.M.[Muhammad Moazam],
VR-PROUD: Vehicle Re-identification using PROgressive Unsupervised Deep architecture,
PR(90), 2019, pp. 52-65.
Elsevier DOI 1903
BibRef
Earlier:
DUPL-VR: Deep Unsupervised Progressive Learning for Vehicle Re-Identification,
ISVC18(286-295).
Springer DOI 1811
Vehicle re-id, Deep learning, Unsupervised, Clustering, Visual surveillance, Progressive learning, Self pace BibRef

Khan, S.D.[Sultan Daud], Ullah, H.[Habib],
A survey of advances in vision-based vehicle re-identification,
CVIU(182), 2019, pp. 50-63.
Elsevier DOI 1905
Survey, Vehicle Re-Identification. Re-identification, Hand-crafted methods, Convolutional neural network, Traffic analysis BibRef

Wu, F.Y.[Fang-Yu], Yan, S.Y.[Shi-Yang], Smith, J.S.[Jeremy S.], Zhang, B.L.[Bai-Ling],
Vehicle re-identification in still images: Application of semi-supervised learning and re-ranking,
SP:IC(76), 2019, pp. 261-271.
Elsevier DOI 1906
Vehicle re-identification, Convolutional neural networks, Semi-supervised learning, Re-ranking BibRef

Lou, Y.H.[Yi-Hang], Bai, Y.[Yan], Liu, J.[Jun], Wang, S.Q.[Shi-Qi], Duan, L.Y.[Ling-Yu],
Embedding Adversarial Learning for Vehicle Re-Identification,
IP(28), No. 8, August 2019, pp. 3794-3807.
IEEE DOI 1907
embedded systems, image sampling, learning (artificial intelligence), cross-view generation, cross-view BibRef

Guo, H., Zhu, K., Tang, M., Wang, J.,
Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification,
IP(28), No. 9, Sep. 2019, pp. 4328-4338.
IEEE DOI 1908
cameras, feature extraction, learning (artificial intelligence), object recognition, traffic engineering computing, feature embedding BibRef

Ooi, H.L.[Hui-Lee], Bilodeau, G.A.[Guillaume-Alexandre], Saunier, N.[Nicolas],
Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection,
ICIAR19(I:195-206).
Springer DOI 1909
BibRef

Kan, S.C.[Shi-Chao], Cen, Y.G.[Yi-Gang], He, Z.H.[Zhi-Hai], Zhang, Z.[Zhi], Zhang, L.[Linna], Wang, Y.H.[Yan-Hong],
Supervised Deep Feature Embedding With Handcrafted Feature,
IP(28), No. 12, December 2019, pp. 5809-5823.
IEEE DOI 1909
Measurement, Image retrieval, Feature extraction, Fuses, Task analysis, Training, Neural networks, Deep feature embedding, vehicle re-identification BibRef

Liu, X., Zhang, S., Wang, X., Hong, R., Tian, Q.,
Group-Group Loss-Based Global-Regional Feature Learning for Vehicle Re-Identification,
IP(29), 2020, pp. 2638-2652.
IEEE DOI 2001
Vehicle re-identification, CNN, global-regional feature learning, distance metric learning BibRef

Zhu, J., Zeng, H., Huang, J., Liao, S., Lei, Z., Cai, C., Zheng, L.,
Vehicle Re-Identification Using Quadruple Directional Deep Learning Features,
ITS(21), No. 1, January 2020, pp. 410-420.
IEEE DOI 2001
Deep learning, Feature extraction, Convolutional neural networks, Databases, Measurement, Cameras, image classification BibRef

Zhao, Y., Shen, C., Wang, H., Chen, S.,
Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval,
ITS(21), No. 2, February 2020, pp. 723-734.
IEEE DOI 2002
Feature extraction, Automobiles, Task analysis, Licenses, Cameras, Proposals, Surveillance, Vehicle attribute detection, vehicle retrieval BibRef

Španhel, J.[Jakub], Sochor, J.[Jakub], Juránek, R.[Roman], Dobeš, P.[Petr], Bartl, V.[Vojtech], Herout, A.[Adam],
Learning feature aggregation in temporal domain for re-identification,
CVIU(192), 2020, pp. 102883.
Elsevier DOI 2002
BibRef

Zapletal, D., Herout, A.,
Vehicle Re-identification for Automatic Video Traffic Surveillance,
Traffic16(1568-1574)
IEEE DOI 1612
BibRef

Tumrani, S.[Saifullah], Deng, Z.Y.[Zhi-Yi], Lin, H.Y.[Hao-Yang], Shao, J.[Jie],
Partial attention and multi-attribute learning for vehicle re-identification,
PRL(138), 2020, pp. 290-297.
Elsevier DOI 1806
Vehicle re-identification, Keypoint detection, Multi-branch network BibRef

Wang, Y.F.[Yue-Feng], Li, H.D.[Hua-Dong], Wei, Y.[Ying], Wang, C.Y.[Chu-Yuan], Wang, L.[Lin],
Vehicle re-identification based on unsupervised local area detection and view discrimination,
IVC(104), 2020, pp. 104008.
Elsevier DOI 2012
Vehicle re-identification, Unsupervised, Discriminatory local area, View discrimination, Cross-view BibRef

Wang, H., Peng, J., Chen, D., Jiang, G., Zhao, T., Fu, X.,
Attribute-Guided Feature Learning Network for Vehicle Reidentification,
MultMedMag(27), No. 4, October 2020, pp. 112-121.
IEEE DOI 2012
Task analysis, Image color analysis, Training, Feature extraction, Smoothing methods, Visualization, Frequency modulation, Attribute-based Label Smoothing Loss BibRef

Chen, X., Sui, H., Fang, J., Feng, W., Zhou, M.,
Vehicle Re-Identification Using Distance-Based Global and Partial Multi-Regional Feature Learning,
ITS(22), No. 2, February 2021, pp. 1276-1286.
IEEE DOI 2102
Spatiotemporal phenomena, Visualization, Cameras, Feature extraction, Interference, vehicle re-identification BibRef

Teng, S., Zhang, S., Huang, Q., Sebe, N.,
Multi-View Spatial Attention Embedding for Vehicle Re-Identification,
CirSysVideo(31), No. 2, February 2021, pp. 816-827.
IEEE DOI 2102
Feature extraction, Task analysis, Measurement, Visualization, Computer science, Training, Neural networks, multi-view BibRef

Jin, Y.[Yi], Li, C.N.[Chen-Ning], Li, Y.D.[Yi-Dong], Peng, P.X.[Pei-Xi], Giannopoulos, G.A.[George A.],
Model Latent Views with Multi-Center Metric Learning for Vehicle Re-Identification,
ITS(22), No. 3, March 2021, pp. 1919-1931.
IEEE DOI 2103
Feature extraction, Visualization, Training, Measurement, Annotations, Task analysis, Semantics, Multi-view modeling, multi-view vehicle re-identification BibRef

Hsu, H.M.[Hung-Min], Cai, J.R.[Jia-Rui], Wang, Y.[Yizhou], Hwang, J.N.[Jenq-Neng], Kim, K.J.[Kwang-Ju],
Multi-Target Multi-Camera Tracking of Vehicles Using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model,
IP(30), 2021, pp. 5198-5210.
IEEE DOI 2106
Cameras, Trajectory, Target tracking, Task analysis, Metadata, Feature extraction, Image color analysis, MTMCT, hierarchical clustering BibRef

Roman-Jimenez, G.[Geoffrey], Guyot, P.[Patrice], Malon, T.[Thierry], Chambon, S.[Sylvie], Charvillat, V.[Vincent], Crouzil, A.[Alain], Péninou, A.[André], Pinquier, J.[Julien], Sedes, F.[Florence], Sénac, C.[Christine],
Improving vehicle re-identification using CNN latent spaces: Metrics comparison and track-to-track extension,
IET-CV(15), No. 2, 2021, pp. 85-98.
DOI Link 2106
BibRef

Boukerche, A.[Azzedine], Ma, X.[Xiren],
Vision-Based Autonomous Vehicle Recognition: A New Challenge for Deep Learning-Based Systems,
Surveys(54), No. 4, May 2021, pp. xx-yy.
DOI Link 2107
Deep learning, vehicle make and model recognition, vehicle re-identification, video surveillance, fine-grained recognition BibRef

Zheng, Z.D.[Zhe-Dong], Ruan, T.[Tao], Wei, Y.C.[Yun-Chao], Yang, Y.[Yi], Mei, T.[Tao],
VehicleNet: Learning Robust Visual Representation for Vehicle Re-Identification,
MultMed(23), 2021, pp. 2683-2693.
IEEE DOI 2109
Training, Robustness, Adaptation models, Data models, Automobiles, Cameras, Feature extraction, Vehicle re-identification, convolutional neural networks BibRef

Zheng, Z.D.[Zhe-Dong], Jiang, M.Y.[Min-Yue], Wang, Z.G.[Zhi-Gang], Wang, J.[Jian], Bai, Z.C.[Ze-Chen], Zhang, X.M.[Xuan-Meng], Yu, X.[Xin], Tan, X.[Xiao], Yang, Y.[Yi], Wen, S.L.[Shi-Lei], Ding, E.[Errui],
Going Beyond Real Data: A Robust Visual Representation for Vehicle Re-identification,
City20(2550-2558)
IEEE DOI 2008
Training, Visualization, Robustness, Feature extraction, Task analysis, Image color analysis, Fuses BibRef

Peng, J.J.[Jin-Jia], Jiang, G.Q.[Guang-Qi], Wang, H.B.[Hui-Bing],
Generalized multiple sparse information fusion for vehicle re-identification,
JVCIR(79), 2021, pp. 103207.
Elsevier DOI 2109
Vehicle re-identification, Hierarchical attention network, Multi-views BibRef


Liu, C.[Chong], Zhang, Y.[Yuqi], Luo, H.[Hao], Tang, J.[Jiasheng], Chen, W.H.[Wei-Hua], Xu, X.[Xianzhe], Wang, F.[Fan], Li, H.[Hao], Shen, Y.D.[Yi-Dong],
City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones,
AICity21(4124-4132)
IEEE DOI 2109
Matched filters, Target tracking, Urban areas, Feature extraction, Cameras BibRef

Li, Y.L.[Yun-Lun], Chin, Z.Y.[Zhi-Yi], Chang, M.C.[Ming-Ching], Chiang, C.K.[Chen-Kuo],
Multi-Camera Tracking By Candidate Intersection Ratio Tracklet Matching,
AICity21(4098-4106)
IEEE DOI 2109
Measurement, Filtering, Smart cities, Visual analytics, Vehicle detection, Pipelines, Streaming media BibRef

Wu, M.H.[Ming-Hu], Qian, Y.Q.[Ye-Qiang], Wang, C.X.[Chun-Xiang], Yang, M.[Ming],
A Multi-Camera Vehicle Tracking System based on City-Scale Vehicle Re-ID and Spatial-Temporal Information,
AICity21(4072-4081)
IEEE DOI 2109
Visualization, Uncertainty, Urban areas, Lighting, Feature extraction, Robustness, Data models BibRef

Yang, K.S.[Kai-Siang], Chen, Y.K.[Yu-Kai], Chen, T.S.[Tsai-Shien], Liu, C.T.[Chih-Ting], Chien, S.Y.[Shao-Yi],
Tracklet-refined Multi-Camera Tracking based on Balanced Cross-Domain Re-Identification for Vehicles,
AICity21(3978-3987)
IEEE DOI 2109
Training, Target tracking, Training data, Filtering algorithms, Information filters, Data models BibRef

Jiang, M.[Minyue], Zhang, X.[Xuanmeng], Yu, Y.[Yue], Bai, Z.[Zechen], Zheng, Z.[Zhedong], Wang, Z.G.[Zhi-Gang], Wang, J.[Jian], Tan, X.[Xiao], Sun, H.[Hao], Ding, E.[Errui], Yang, Y.[Yi],
Robust Vehicle Re-identification via Rigid Structure Prior,
AICity21(4021-4028)
IEEE DOI 2109
Geometry, Visualization, Matched filters, Scalability, Urban areas, Lighting, Feature extraction BibRef

Luo, H.[Hao], Chen, W.H.[Wei-Hua], Xu, X.[Xianzhe], Gu, J.Y.[Jian-Yang], Zhang, Y.[Yuqi], Liu, C.[Chong], Jiang, Y.[Yiqi], He, S.[Shuting], Wang, F.[Fan], Li, H.[Hao],
An Empirical Study of Vehicle Re-Identification on the AI City Challenge,
AICity21(4090-4097)
IEEE DOI 2109
Training, Computational modeling, Urban areas, Training data, Data models BibRef

Huynh, S.V.[Su V.], Nguyen, N.H.[Nam H.], Nguyen, N.T.[Ngoc T.], Nguyen, V.T.[Vinh Tq.], Huynh, C.[Chau], Nguyen, C.[Chuong],
A Strong Baseline for Vehicle Re-Identification,
AICity21(4142-4149)
IEEE DOI 2109
Training, Target tracking, Head, Urban areas, Stacking BibRef

Sun, Y.L.[Yong-Li], Li, W.[Wenpeng], Wei, H.[Hua], Zhang, L.[Longtao], Tian, J.[Jiahao], Sun, G.Z.[Guang-Ze], Wang, G.[Gang], Cao, J.L.[Jun-Liang], Zhao, Z.[Zhifeng], Ding, J.F.[Jun-Feng],
Progressive Data Mining and Adaptive Weighted Multi-Model Ensemble for Vehicle Re-Identification,
AICity21(4196-4201)
IEEE DOI 2109
Training, Adaptation models, Computational modeling, Image matching, Urban areas, Lighting, Data models BibRef

Kamenou, E.[Eleni], del Rincon, J.M.[Jesus Martinez], Miller, P.[Paul], Devlin-Hill, P.[Patricia],
Multi-level Deep Learning Vehicle Re-identification using Ranked-based Loss Functions,
ICPR21(9099-9106)
IEEE DOI 2105
Measurement, Space vehicles, Deep learning, Annotations, System performance, Cameras, Pattern recognition BibRef

Xu, Z.[Zheming], Wei, L.[Lili], Lang, C.[Congyan], Feng, S.[Songhe], Wang, T.[Tao], Bors, A.G.[Adrian G.],
HSS-GCN: A Hierarchical Spatial Structural Graph Convolutional Network for Vehicle Re-identification,
IUC20(356-364).
Springer DOI 2103
BibRef

Xie, Y., Zhu, J., Zeng, H., Cai, C., Zheng, L.,
Learning Matching Behavior Differences for Compressing Vehicle Re-identification Models,
VCIP20(523-526)
IEEE DOI 2102
Training, Testing, Probes, Image coding, Loss measurement, Computational modeling, Trajectory, Deep Learning, Vehicle Re-identification BibRef

Khorramshahi, P.[Pirazh], Peri, N.[Neehar], Chen, J.C.[Jun-Cheng], Chellappa, R.[Rama],
The Devil Is in the Details: Self-supervised Attention for Vehicle Re-identification,
ECCV20(XIV:369-386).
Springer DOI 2011
BibRef

Chen, T.S.[Tsai-Shien], Liu, C.T.[Chih-Ting], Wu, C.W.[Chih-Wei], Chien, S.Y.[Shao-Yi],
Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network,
ECCV20(II:330-346).
Springer DOI 2011
BibRef

Lee, S., Park, E., Yi, H., Lee, S.H.,
StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identification,
City20(2590-2597)
IEEE DOI 2008
Adaptation models, Feature extraction, Task analysis, Training, Data models, Image color analysis, Urban areas BibRef

Meng, D., Li, L., Liu, X., Li, Y., Yang, S., Zha, Z., Gao, X., Wang, S., Huang, Q.,
Parsing-Based View-Aware Embedding Network for Vehicle Re-Identification,
CVPR20(7101-7110)
IEEE DOI 2008
Feature extraction, Task analysis, Cameras, Training, Image color analysis, Fuses BibRef

Chen, T., Lee, M., Liu, C., Chien, S.,
Viewpoint-aware Channel-wise Attentive Network for Vehicle Re-identification,
City20(2448-2455)
IEEE DOI 2008
Feature extraction, Estimation, Cameras, Task analysis, Semantics, Detectors, Data mining BibRef

Fernández, M.[Marta], Moral, P.[Paula], García-Martín, Á.[Álvaro], Martínez, J.M.[José M.],
Vehicle Re-Identification based on Ensembling Deep Learning Features including a Synthetic Training Dataset, Orientation and Background Features, and Camera Verification.,
AICity21(4063-4071)
IEEE DOI 2109
BibRef
Earlier: A2, A3, A4, Only:
Vehicle Re-Identification in Multi-Camera scenarios based on Ensembling Deep Learning Features,
City20(2574-2580)
IEEE DOI 2008
Training, Deep learning, Image resolution, Image color analysis, Cameras, Feature extraction, Pattern recognition. Feature extraction, Cameras, Trajectory, Task analysis, Training, Urban areas, Servers BibRef

Zhu, X., Luo, Z., Fu, P., Ji, X.,
VOC-RelD: Vehicle Re-identification based on Vehicle-Orientation-Camera,
City20(2566-2573)
IEEE DOI 2008
Cameras, Shape, Training, Task analysis, Feature extraction, Image color analysis, Urban areas BibRef

Gao, C., Hu, Y., Zhang, Y., Yao, R., Zhou, Y., Zhao, J.,
Vehicle Re-Identification Based on Complementary Features,
City20(2520-2526)
IEEE DOI 2008
Feature extraction, Training, Task analysis, Encoding, Testing, Information filters BibRef

Sebastian, C., Imbriaco, R., Bondarev, E., de With, P.H.N.,
Dual Embedding Expansion for Vehicle Re-identification,
City20(2475-2484)
IEEE DOI 2008
Feature extraction, Task analysis, Image retrieval, Image color analysis, Computational modeling, Frequency modulation BibRef

He, S., Luo, H., Chen, W., Zhang, M., Zhang, Y., Wang, F., Li, H., Jiang, W.,
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification,
City20(2485-2493)
IEEE DOI 2008
Data models, Task analysis, Testing, Feature extraction, Urban areas, Data mining, Computer vision BibRef

Liu, K., Xu, Z., Hou, Z., Zhao, Z., Su, F.,
Further Non-local and Channel Attention Networks for Vehicle Re-identification,
City20(2494-2500)
IEEE DOI 2008
Feature extraction, Task analysis, Training, Kernel, Visualization, Network architecture, Convolution BibRef

Eckstein, V., Schumann, A., Specker, A.,
Large Scale Vehicle Re-Identification by Knowledge Transfer from Simulated Data and Temporal Attention,
City20(2626-2631)
IEEE DOI 2008
Data models, Cameras, Task analysis, Adaptation models, Computational modeling, Machine learning, Visualization BibRef

Zhuge, C., Peng, Y., Li, Y., Ai, J., Chen, J.,
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification,
City20(2632-2637)
IEEE DOI 2008
Feature extraction, Training, Image color analysis, Robustness, Cameras, Task analysis, Automobiles BibRef

Chu, R.H.[Rui-Hang], Sun, Y.F.[Yi-Fan], Li, Y.D.[Ya-Dong], Liu, Z.[Zheng], Zhang, C.[Chi], Wei, Y.C.[Yi-Chen],
Vehicle Re-Identification with Viewpoint-Aware Metric Learning,
ICCV19(8281-8290)
IEEE DOI 2004
image recognition, learning (artificial intelligence), road vehicles, traffic engineering computing, similar viewpoints, Face recognition BibRef

Khorramshahi, P., Kumar, A., Peri, N., Rambhatla, S.S., Chen, J., Chellappa, R.,
A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification,
ICCV19(6131-6140)
IEEE DOI 2004
Code, Re-Identification.
WWW Link. feature extraction, learning (artificial intelligence), vehicle re-identification, attention models, Task analysis BibRef

Wang, P., Jiao, B., Yang, L., Yang, Y., Zhang, S., Wei, W., Zhang, Y.,
Vehicle Re-Identification in Aerial Imagery: Dataset and Approach,
ICCV19(460-469)
IEEE DOI 2004
image processing, traffic engineering computing, aerial imagery, UAV-mounted cameras, UAV-based vehicle ReID dataset, Visualization BibRef

Tang, Z., Naphade, M., Birchfield, S., Tremblay, J., Hodge, W., Kumar, R., Wang, S., Yang, X.,
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data,
ICCV19(211-220)
IEEE DOI 2004
feature extraction, image classification, image representation, image segmentation, learning (artificial intelligence), Solid modeling BibRef

Wu, M.J.[Ming-Jie], Zhang, Y.F.[Yong-Fei], Zhang, T.Y.[Tian-Yu], Zhang, W.Q.[Wen-Qi],
Background Segmentation for Vehicle Re-identification,
MMMod20(II:88-99).
Springer DOI 2003
BibRef

Lou, Y.H.[Yi-Hang], Bai, Y.[Yan], Liu, J.[Jun], Wang, S.Q.[Shi-Qi], Duan, L.Y.[Ling-Yu],
VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild,
CVPR19(3230-3238).
IEEE DOI 2002
BibRef

He, B.[Bing], Li, J.[Jia], Zhao, Y.[Yifan], Tian, Y.H.[Yong-Hong],
Part-Regularized Near-Duplicate Vehicle Re-Identification,
CVPR19(3992-4000).
IEEE DOI 2002
BibRef

Yang, X., Lang, C., Peng, P., Xing, J.,
Vehicle Re-Identification by Multi-Grain Learni,
ICIP19(3113-3117)
IEEE DOI 1910
Vehicle re-identification, Multi-grain ranking loss BibRef

Alfasly, S.A.S., Hu, Y., Liang, T., Jin, X., Zhao, Q., Liu, B.,
Variational Representation Learning for Vehicle Re-Identificati,
ICIP19(3118-3122)
IEEE DOI 1910
Deep Learning, LSTM, Variational Features, Vehicle Re-Identification BibRef

de Oliveira, I.O., Fonseca, K.V.O., Minetto, R.,
A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras,
ICIP19(669-673)
IEEE DOI 1910
Vehicle Re-identification, Siamese Neural Networks, Vehicle Matching, Travel Time Estimation BibRef

Wei, X.S.[Xiu-Shen], Zhang, C.L.[Chen-Lin], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], Wu, J.X.[Jian-Xin],
Coarse-to-Fine: A RNN-Based Hierarchical Attention Model for Vehicle Re-identification,
ACCV18(II:575-591).
Springer DOI 1906
BibRef

Zhong, X.[Xian], Feng, M.[Meng], Huang, W.X.[Wen-Xin], Wang, Z.[Zheng], Satoh, S.[Shin'Ichi],
Poses Guide Spatiotemporal Model for Vehicle Re-identification,
MMMod19(II:426-439).
Springer DOI 1901
BibRef

Wu, F., Yan, S., Smith, J.S., Zhang, B.,
Joint Semi-supervised Learning and Re-ranking for Vehicle Re-identification,
ICPR18(278-283)
IEEE DOI 1812
Training, Probes, Feature extraction, Semisupervised learning, Generative adversarial networks, Smoothing methods, Cameras BibRef

Marín-Reyes, P.A., Bergamini, L.[Luca], Lorenzo-Navarro, J., Palazzi, A.[Andrea], Calderara, S.[Simone], Cucchiara, R.[Rita],
Unsupervised Vehicle Re-identification Using Triplet Networks,
City18(166-1665)
IEEE DOI 1812
Videos, Urban areas, Task analysis, Artificial intelligence, Detectors, Surveillance, Cameras BibRef

Porrello, A.[Angelo], Bergamini, L.[Luca], Calderara, S.[Simone],
Robust Re-identification by Multiple Views Knowledge Distillation,
ECCV20(X:93-110).
Springer DOI 2011
Code, Re-Identification.
WWW Link. BibRef

Li, S., Pei, M., Zhu, L.,
Vehicle Re-Identification by Deep Feature Fusion Based on Joint Bayesian Criterion,
ICPR18(2032-2037)
IEEE DOI 1812
Feature extraction, Bayes methods, Fuses, Licenses, Training, Training data, Task analysis BibRef

Zhu, J., Zeng, H., Lei, Z., Liao, S., Zheng, L., Cai, C.,
A Shortly and Densely Connected Convolutional Neural Network for Vehicle Re-identification,
ICPR18(3285-3290)
IEEE DOI 1812
Convolutional neural networks, Linear programming, Feature extraction, Training, Cameras, Surveillance BibRef

Wu, C., Liu, C., Chiang, C., Tu, W., Chien, S.,
Vehicle Re-identification with the Space-Time Prior,
City18(121-1217)
IEEE DOI 1812
Feature extraction, Videos, Automobiles, Task analysis, Urban areas, Visualization, Testing BibRef

Jiang, N., Xu, Y., Zhou, Z., Wu, W.,
Multi-Attribute Driven Vehicle Re-Identification with Spatial-Temporal Re-Ranking,
ICIP18(858-862)
IEEE DOI 1809
Feature extraction, Image color analysis, Computer architecture, Machine learning, Probes, Lighting, Cameras, spatial-temporal re-ranking BibRef

Kanaci, A.[Aytaç], Zhu, X.T.[Xia-Tian], Gong, S.G.[Shao-Gang],
Vehicle Re-identification in Context,
GCPR18(377-390).
Springer DOI 1905
BibRef

Cui, C., Sang, N., Gao, C., Zou, L.,
Vehicle re-identification by fusing multiple deep neural networks,
IPTA17(1-6)
IEEE DOI 1804
convolution, feature extraction, feedforward neural nets, image classification, image colour analysis, image fusion, Vehicle re-identification BibRef

Tang, Y., Wu, D., Jin, Z., Zou, W., Li, X.,
Multi-modal metric learning for vehicle re-identification in traffic surveillance environment,
ICIP17(2254-2258)
IEEE DOI 1803
Cameras, Feature extraction, Image color analysis, Measurement, Robustness, Surveillance, Training, Convolutional Neural Network, Vehicle Re-identification BibRef

Li, Y.Q.[Yu-Qi], Li, Y.H.[Yang-Hao], Yan, H.F.[Hong-Fei], Liu, J.Y.[Jia-Ying],
Deep joint discriminative learning for vehicle re-identification and retrieval,
ICIP17(395-399)
IEEE DOI 1803
Computational modeling, Face recognition, Feature extraction, Image recognition, Machine learning, Task analysis, Training, Vehicle Retrieval BibRef

Shen, Y., Xiao, T., Li, H., Yi, S., Wang, X.,
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals,
ICCV17(1918-1927)
IEEE DOI 1802
computer vision, feature extraction, image retrieval, intelligent transportation systems, Visualization BibRef

Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J., Yan, J., Wang, S., Li, H., Wang, X.,
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification,
ICCV17(379-387)
IEEE DOI 1802
feature extraction, image retrieval, spatiotemporal phenomena, traffic engineering computing, feature extraction, Wheels BibRef

Cormier, M., Sommer, L.W., Teutsch, M.,
Low resolution vehicle re-identification based on appearance features for wide area motion imagery,
CVAST16(1-7)
IEEE DOI 1606
image colour analysis BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Vehicle Tracking, Speed Computations, Vehicle Speed, Traffic Speed .


Last update:Sep 12, 2021 at 22:38:33