16.7.2.5.2 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, 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

Messoussi, O.[Oumayma], de Magalhăes, F.G.[Felipe Gohring], Lamarre, F.[Francois], Perreault, F.[Francis], Sogoba, I.[Ibrahima], Bilodeau, G.A.[Guillaume-Alexandre], Nicolescu, G.[Gabriela],
Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes,
ISVC21(II:191-202).
Springer DOI 2112
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.Z.[Yi-Zhou], 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.R.[Xi-Ren],
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

Zhu, W.Q.[Wen-Qian], Wang, Z.Y.[Zhong-Yuan], Hu, R.M.[Rui-Min], Li, D.S.[Deng-Shi],
From Semantic to Spatial Awareness: Vehicle Reidentification With Multiple Attention Mechanisms,
MultMedMag(28), No. 3, July 2021, pp. 32-41.
IEEE DOI 2109
Feature extraction, Semantics, Automotive components, Visualization, Video surveillance, Licenses, Vehicular ad hoc networks BibRef

Xiong, Z.X.[Zhong-Xia], Li, M.[Ming], Ma, Y.[Yalong], Wu, X.[Xinkai],
Vehicle Re-Identification With Image Processing and Car-Following Model Using Multiple Surveillance Cameras From Urban Arterials,
ITS(22), No. 12, December 2021, pp. 7619-7630.
IEEE DOI 2112
Cameras, Data mining, Surveillance, Detectors, Trajectory, Feature extraction, Surveillance video, image processing, IDM car-following model BibRef

Lin, X.M.[Xian-Ming], Li, R.[Run], Zheng, X.[Xiawu], Peng, P.[Pai], Wu, Y.J.[Yong-Jian], Huang, F.Y.[Fei-Yue], Ji, R.R.[Rong-Rong],
Aggregating Global and Local Visual Representation for Vehicle Re-IDentification,
MultMed(23), 2021, pp. 3968-3977.
IEEE DOI 2112
Measurement, Visualization, Inspection, Image color analysis, Lighting, Vehicles, Vehicle Re-ID, MCMS-Siam network BibRef

Li, Y.D.[Yi-Dong], Liu, K.[Kai], Jin, Y.[Yi], Wang, T.[Tao], Lin, W.P.[Wei-Peng],
VARID: Viewpoint-Aware Re-IDentification of Vehicle Based on Triplet Loss,
ITS(23), No. 2, February 2022, pp. 1381-1390.
IEEE DOI 2202
Task analysis, Feature extraction, Measurement, Visualization, Benchmark testing, Robustness, Training, Vehicle re-identification, triplet loss BibRef

Chen, Y.B.[Yan-Bing], Ke, W.[Wei], Lin, H.[Hong], Lam, C.T.[Chan-Tong], Lv, K.[Kai], Sheng, H.[Hao], Xiong, Z.[Zhang],
Local perspective based synthesis for vehicle re-identification: A transformation state adversarial method,
JVCIR(83), 2022, pp. 103432.
Elsevier DOI 2202
Vehicle re-identification, Data synthesis, Local-region perspective transformation, Parameter generator network BibRef

Wang, Q.[Qi], Min, W.D.[Wei-Dong], Han, Q.[Qing], Liu, Q.[Qian], Zha, C.[Cheng], Zhao, H.Y.[Hao-Yu], Wei, Z.[Zitai],
Inter-Domain Adaptation Label for Data Augmentation in Vehicle Re-Identification,
MultMed(24), 2022, pp. 1031-1041.
IEEE DOI 2203
Training, Semisupervised learning, Cameras, Training data, Data models, Adaptation models, Smoothing methods, inter-domain adaptation label smoothing regularization BibRef

Huang, Y.[Yue], Liang, B.[Borong], Xie, W.P.[Wei-Ping], Liao, Y.H.[Ying-Hao], Kuang, Z.Y.[Zhen-Yu], Zhuang, Y.H.[Yi-Hong], Ding, X.H.[Xing-Hao],
Dual Domain Multi-Task Model for Vehicle Re-Identification,
ITS(23), No. 4, April 2022, pp. 2991-2999.
IEEE DOI 2204
Image color analysis, Task analysis, Feature extraction, Automobiles, Training, Licenses, Frequency-domain analysis, vehicle re-identification BibRef

Shen, F.[Fei], Zhu, J.Q.[Jian-Qing], Zhu, X.B.[Xia-Bin], Xie, Y.[Yi], Huang, J.C.[Jing-Chang],
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-Identification,
ITS(23), No. 7, July 2022, pp. 8793-8804.
IEEE DOI 2207
Feature extraction, Detectors, Annotations, Visualization, Task analysis, Lighting, Deep learning, graph network, vehicle re-identification BibRef

Tu, J.Z.[Jing-Zheng], Chen, C.[Cailian], Huang, X.L.[Xiao-Lin], He, J.P.[Jian-Ping], Guan, X.P.[Xin-Ping],
DFR-ST: Discriminative feature representation with spatio-temporal cues for vehicle re-identification,
PR(131), 2022, pp. 108887.
Elsevier DOI 2208
Vehicle re-identification, Computer vision, Deep learning, Attention mechanism, Video surveillance BibRef

Zheng, A.[Aihua], Sun, X.[Xia], Li, C.L.[Cheng-Long], Tang, J.[Jin],
Viewpoint-Aware Progressive Clustering for Unsupervised Vehicle Re-Identification,
ITS(23), No. 8, August 2022, pp. 11422-11435.
IEEE DOI 2208
Task analysis, Clustering algorithms, Unsupervised learning, Cameras, Annotations, Space vehicles, Shape, Viewpoint-aware, unsupervised learning BibRef

Zhang, C.[Cheng], Chen, B.Y.[Bi Yu], Lam, W.H.K.[William H. K.], Ho, H.W., Shi, X.M.[Xiao-Meng], Yang, X.G.[Xiao-Guang], Ma, W.[Wei], Wong, S.C., Chow, A.H.F.[Andy H. F.],
Vehicle Re-identification for Lane-level Travel Time Estimations on Congested Urban Road Networks Using Video Images,
ITS(23), No. 8, August 2022, pp. 12877-12893.
IEEE DOI 2208
Roads, Feature extraction, Estimation, Standards, Detectors, Cameras, Visualization, Vehicle re-identification, video images BibRef

Bai, Y.[Yan], Liu, J.[Jun], Lou, Y.H.[Yi-Hang], Wang, C.[Ce], Duan, L.Y.[Ling-Yu],
Disentangled Feature Learning Network and a Comprehensive Benchmark for Vehicle Re-Identification,
PAMI(44), No. 10, October 2022, pp. 6854-6871.
IEEE DOI 2209
Cameras, Training, Meteorology, Benchmark testing, Surveillance, Lighting, Feature extraction, Vehicle re-identification, disentangled learning BibRef

Li, H.C.[Hong-Chao], Li, C.L.[Cheng-Long], Zheng, A.[Aihua], Tang, J.[Jin], Luo, B.[Bin],
Attribute and State Guided Structural Embedding Network for Vehicle Re-Identification,
IP(31), 2022, pp. 5949-5962.
IEEE DOI 2209
Cameras, Image color analysis, Feature extraction, Task analysis, Space vehicles, Measurement, Force, Vehicle re-identification, global structural embedding BibRef

Sun, W.[Wei], Dai, G.Z.[Guang-Zhao], Zhang, X.R.[Xiao-Rui], He, X.Z.[Xiao-Zheng], Chen, X.[Xuan],
TBE-Net: A Three-Branch Embedding Network With Part-Aware Ability and Feature Complementary Learning for Vehicle Re-Identification,
ITS(23), No. 9, September 2022, pp. 14557-14569.
IEEE DOI 2209
Feature extraction, Cameras, Image color analysis, Information science, Sun, Training, Technological innovation, embedding BibRef

Lu, M.M.[Ming-Ming], Xu, Y.C.[Yong-Chuan], Li, H.F.[Hai-Feng],
Vehicle Re-Identification Based on UAV Viewpoint: Dataset and Method,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Tu, M.F.[Ming-Fei], Zhu, K.[Kuan], Guo, H.Y.[Hai-Yun], Miao, Q.H.[Qing-Hai], Zhao, C.Y.[Chao-Yang], Zhu, G.[Guibo], Qiao, H.[Honglin], Huang, G.[Gaopan], Tang, M.[Ming], Wang, J.Q.[Jin-Qiao],
Multi-Granularity Mutual Learning Network for Object Re-Identification,
ITS(23), No. 9, September 2022, pp. 15178-15189.
IEEE DOI 2209
Feature extraction, Visualization, Task analysis, Intelligent transportation systems, Image reconstruction, solving jigsaw puzzle BibRef

Tran, D.N.N.[Duong Nguyen-Ngoc], Pham, L.H.[Long Hoang], Jeon, H.J.[Hyung-Joon], Nguyen, H.H.[Huy-Hung], Jeon, H.M.[Hyung-Min], Tran, T.H.P.[Tai Huu-Phuong], Jeon, J.W.[Jae Wook],
A Robust Traffic-Aware City-Scale Multi-Camera Vehicle Tracking Of Vehicles,
AICity22(3149-3158)
IEEE DOI 2210
Target tracking, Vehicle detection, Surveillance, Urban areas, Traffic control, Feature extraction, Trajectory BibRef

Li, Z.Y.[Zhi-Yong], Luo, Y.Z.[Yun-Zhong], Li, Q.[Qiaochu], Song, L.[Lulu], Liu, W.Y.[Wei-Yi],
Mixed-attention-based regional soft partition network for vehicle reidentification,
IET-IPR(16), No. 13, 2022, pp. 3648-3658.
DOI Link 2210
BibRef

Yang, H.[Hao], Cai, J.R.[Jia-Rui], Zhu, M.[Meixin], Liu, C.X.[Chen-Xi], Wang, Y.[Yinhai],
Traffic-Informed Multi-Camera Sensing (TIMS) System Based on Vehicle Re-Identification,
ITS(23), No. 10, October 2022, pp. 17189-17200.
IEEE DOI 2210
Sensors, Feature extraction, Roads, Information retrieval, Data mining, Videos, Traffic sensing, vehicle re-identification BibRef

Liu, C.S.[Chun-Sheng], Song, Y.[Ye], Chang, F.L.[Fa-Liang], Li, S.[Shuang], Ke, R.M.[Rui-Min], Wang, Y.H.[Yin-Hai],
Posture Calibration Based Cross-View and Hard-Sensitive Metric Learning for UAV-Based Vehicle Re-Identification,
ITS(23), No. 10, October 2022, pp. 19246-19257.
IEEE DOI 2210
Measurement, Feature extraction, Calibration, Training, Autonomous aerial vehicles, Surveillance, cross-view re-identification BibRef

Li, H.C.[Hong-Chao], Li, C.L.[Cheng-Long], Zheng, A.[Aihua], Tang, J.[Jin], Luo, B.[Bin],
MsKAT: Multi-Scale Knowledge-Aware Transformer for Vehicle Re-Identification,
ITS(23), No. 10, October 2022, pp. 19557-19568.
IEEE DOI 2210
Transformers, Visualization, Feature extraction, Image color analysis, Semantics, Knowledge based systems, multi-scale BibRef

Lu, Z.F.[Ze-Feng], Lin, R.H.[Rong-Hao], Lou, X.[Xulei], Zheng, L.F.[Li-Feng], Hu, H.F.[Hai-Feng],
Identity-Unrelated Information Decoupling Model for Vehicle Re-Identification,
ITS(23), No. 10, October 2022, pp. 19001-19015.
IEEE DOI 2210
Feature extraction, Transformers, Task analysis, Generative adversarial networks, Image color analysis, information decoupling BibRef

Qian, W.[Wen], He, Z.[Zhiqun], Chen, C.[Chen], Peng, S.[Silong],
Navigating Diverse Salient Features for Vehicle Re-Identification,
ITS(23), No. 12, December 2022, pp. 24578-24587.
IEEE DOI 2212
Navigation, Task analysis, Image color analysis, Boosting, Feature extraction, Benchmark testing, Space vehicles, cross-space constraints BibRef

Tang, L.[Lisha], Wang, Y.[Yi], Chau, L.P.[Lap-Pui],
Weakly-Supervised Part-Attention and Mentored Networks for Vehicle Re-Identification,
CirSysVideo(32), No. 12, December 2022, pp. 8887-8898.
IEEE DOI 2212
Feature extraction, Representation learning, Location awareness, Annotations, Lighting, Clutter, Vehicle re-identification, multi-task learning BibRef

Zhu, W.Q.[Wen-Qian], Wang, Z.Y.[Zhong-Yuan], Wang, X.C.[Xiao-Chen], Hu, R.[Ruimin], Liu, H.[Huikai], Liu, C.[Cheng], Wang, C.[Chao], Li, D.[Dengshi],
A Dual Self-Attention mechanism for vehicle re-Identification,
PR(137), 2023, pp. 109258.
Elsevier DOI 2302
Cross-region attention, Dual self-attention, Multi-attention network, Vehicle re-identification, Feature embedding BibRef

Sun, Z.[Ziruo], Nie, X.[Xiushan], Bi, X.P.[Xiao-Peng], Wang, S.H.[Shao-Hua], Yin, Y.L.[Yi-Long],
Detail enhancement-based vehicle re-identification with orientation-guided re-ranking,
PR(137), 2023, pp. 109304.
Elsevier DOI 2302
Vehicle re-identification, Detail enhancement, Re-ranking BibRef

Lu, Z.F.[Ze-Feng], Lin, R.[Ronghao], Hu, H.F.[Hai-Feng],
MART: Mask-Aware Reasoning Transformer for Vehicle Re-Identification,
ITS(24), No. 2, February 2023, pp. 1994-2009.
IEEE DOI 2302
Feature extraction, Transformers, Data mining, Semantics, Cognition, Training, Task analysis, Vehicle re-identification, transformers, cross-image learning BibRef

Wei, R.[Ran], Gu, J.Y.[Jian-Yang], He, S.[Shuting], Jiang, W.[Wei],
Transformer-Based Domain-Specific Representation for Unsupervised Domain Adaptive Vehicle Re-Identification,
ITS(24), No. 3, March 2023, pp. 2935-2946.
IEEE DOI 2303
Transformers, Task analysis, Adaptation models, Encoding, Training, Representation learning, Benchmark testing, clustering BibRef

Li, M.[Ming], Liu, J.[Jun], Zheng, C.[Ce], Huang, X.M.[Xin-Ming], Zhang, Z.M.[Zi-Ming],
Exploiting Multi-View Part-Wise Correlation via an Efficient Transformer for Vehicle Re-Identification,
MultMed(25), 2023, pp. 919-929.
IEEE DOI 2303
Transformers, Correlation, Feature extraction, Visualization, Training, Benchmark testing, Task analysis, Correlation exploiting, vehicle re-identification BibRef

Li, W.[Wei], Guo, H.Y.[Hai-Yun], Dong, H.H.[Hong-Hui], Tang, M.[Ming], Zhou, Y.[Yue], Wang, J.Q.[Jin-Qiao],
Bi-Level Implicit Semantic Data Augmentation for Vehicle Re-Identification,
ITS(24), No. 4, April 2023, pp. 4364-4376.
IEEE DOI 2304
Semantics, Training, Space vehicles, Data models, Feature extraction, Training data, Robustness, Vehicle re-identification, triplet-based ranking constraint BibRef

Wang, Q.[Qi], Zhong, Y.L.[Yu-Ling], Min, W.D.[Wei-Dong], Zhao, H.Y.[Hao-Yu], Gai, D.[Di], Han, Q.[Qing],
Dual similarity pre-training and domain difference encouragement learning for vehicle re-identification in the wild,
PR(139), 2023, pp. 109513.
Elsevier DOI 2304
Vehicle re-identification, Unsupervised domain adaptation, Dual constraint label smoothing regularization loss, Pseudo label refinement BibRef

Lu, Z.F.[Ze-Feng], Lin, R.[Ronghao], He, Q.[Qiaolin], Hu, H.F.[Hai-Feng],
Mask-Aware Pseudo Label Denoising for Unsupervised Vehicle Re-Identification,
ITS(24), No. 4, April 2023, pp. 4333-4347.
IEEE DOI 2304
Feature extraction, Cameras, Space vehicles, Task analysis, Supervised learning, Noise reduction, Adaptation models, pseudo labels refining BibRef

Zhang, Q.[Qian], Zhang, M.X.[Ming-Xin], Liu, J.[Jinghe], He, X.Y.[Xuan-Yu], Song, R.[Ran], Zhang, W.[Wei],
Unsupervised Maritime Vessel Re-Identification With Multi-Level Contrastive Learning,
ITS(24), No. 5, May 2023, pp. 5406-5418.
IEEE DOI 2305
Training, Task analysis, Cameras, Unsupervised learning, Surveillance, Representation learning, unsupervised learning BibRef

Meng, D.[Dechao], Li, L.[Liang], Liu, X.J.[Xue-Jing], Gao, L.[Lin], Huang, Q.M.[Qing-Ming],
Viewpoint Alignment and Discriminative Parts Enhancement in 3D Space for Vehicle ReID,
MultMed(25), 2023, pp. 2954-2965.
IEEE DOI 2309
BibRef

Chen, Y.B.[Yong-Biao], Zhang, S.[Sheng], Liu, F.X.[Fang-Xin], Wu, C.G.[Cheng-Gang], Guo, K.C.[Kai-Cheng], Qi, Z.W.[Zheng-Wei],
DVHN: A Deep Hashing Framework for Large-Scale Vehicle Re-Identification,
ITS(24), No. 9, September 2023, pp. 9268-9280.
IEEE DOI 2310
BibRef

Zheng, A.[Aihua], Zhang, C.B.[Chao-Bin], Li, C.L.[Cheng-Long], Tang, J.[Jin], Tan, C.[Chang],
Multi-Query Vehicle Re-Identification: Viewpoint-Conditioned Network, Unified Dataset and New Metric,
IP(32), 2023, pp. 5948-5960.
IEEE DOI Code:
WWW Link. 2311
BibRef

He, Q.[Qiaolin], Lu, Z.F.[Ze-Feng], Wang, Z.[Zihan], Hu, H.F.[Hai-Feng],
Graph-Based Progressive Fusion Network for Multi-Modality Vehicle Re-Identification,
ITS(24), No. 11, November 2023, pp. 12431-12447.
IEEE DOI 2311
BibRef

Pang, X.[Xiyu], Tian, X.[Xin], Nie, X.[Xiushan], Yin, Y.L.[Yi-Long], Jiang, G.[Gangwu],
Vehicle re-identification based on grouping aggregation attention and cross-part interaction,
JVCIR(97), 2023, pp. 103937.
Elsevier DOI 2312
Vehicle re-identification, Grouping aggregation attention, Cross-part interaction BibRef

Kuang, Z.Y.[Zhen-Yu], He, C.[Chuchu], Huang, Y.[Yue], Ding, X.[Xinghao], Li, H.F.[Hua-Feng],
Joint Image and Feature Levels Disentanglement for Generalizable Vehicle Re-identification,
ITS(24), No. 12, December 2023, pp. 15259-15273.
IEEE DOI 2312
BibRef

Lian, J.W.[Jia-Wei], Wang, D.H.[Da-Han], Wu, Y.[Yun], Zhu, S.Z.[Shun-Zhi],
Multi-Branch Enhanced Discriminative Network for Vehicle Re-Identification,
ITS(25), No. 2, February 2024, pp. 1263-1274.
IEEE DOI 2402
Feature extraction, Task analysis, Data mining, Pedestrians, Annotations, Manuals, Vehicle dynamics, Vehicle re-identification, intelligent transportation systems BibRef

Huang, J.H.[Jun-Hao], Deng, Y.H.[Yu-Hui], Wang, K.[Ke], Li, Z.W.[Zhang-Wei], Tang, Z.M.[Zhi-Min], Ding, W.P.[Wei-Ping],
UnbiasNet: Vehicle Re-Identification Oriented Unbiased Feature Enhancement by Using Causal Effect,
ITS(25), No. 2, February 2024, pp. 1925-1937.
IEEE DOI 2402
Training, Feature extraction, Computational modeling, Task analysis, Adaptation models, Benchmark testing, Annotations BibRef

Li, H.C.[Hong-Chao], Zheng, A.[Aihua], Sun, L.P.[Li-Ping], Luo, Y.[Yonglong],
Camera Topology Graph Guided Vehicle Re-Identification,
MultMed(26), 2024, pp. 1565-1577.
IEEE DOI 2402
Cameras, Topology, Convolutional neural networks, Network topology, Training, Representation learning, Aggregates, graph convolutional network BibRef

Zhao, Q.Q.[Qian-Qian], Zhan, S.[Simin], Cheng, R.[Rui], Zhu, J.Q.[Jian-Qing], Zeng, H.Q.[Huan-Qiang],
A Benchmark for Vehicle Re-Identification in Mixed Visible and Infrared Domains,
SPLetters(31), 2024, pp. 726-730.
IEEE DOI 2403
Probes, Protocols, Cameras, Detectors, Training, Benchmark testing, Residual neural networks, Vehicle re-identification, benchmark BibRef

Jiao, B.L.[Bing-Liang], Yang, L.[Lu], Gao, L.Y.[Li-Ying], Wang, P.[Peng], Zhang, S.Z.[Shi-Zhou], Zhang, Y.N.[Yan-Ning],
Vehicle Re-Identification in Aerial Images and Videos: Dataset and Approach,
CirSysVideo(34), No. 3, March 2024, pp. 1586-1603.
IEEE DOI Code:
WWW Link. 2403
Autonomous aerial vehicles, Feature extraction, Cameras, Annotations, Vehicle dynamics, Deformation, Adaptation models, attention BibRef


Moral, P.[Paula], García-Martín, Á.[Álvaro], Martínez, J.M.[José M.],
Vehicle Re-identification Based on Unsupervised Domain Adaptation by Incremental Generation of Pseudo-labels,
CIARP23(I:76-89).
Springer DOI 2312
BibRef

Khorramshahi, P.[Pirazh], Shenoy, V.[Vineet], Chellappa, R.[Rama],
Robust and Scalable Vehicle Re-Identification via Self-Supervision,
AICity23(5295-5304)
IEEE DOI 2309
BibRef

Piano, L.[Luca], Pratticň, F.G.[Filippo Gabriele], Russo, A.S.[Alessandro Sebastian], Lanari, L.[Lorenzo], Morra, L.[Lia], Lamberti, F.[Fabrizio],
Bent and Broken Bicycles: Leveraging synthetic data for damaged object re-identification,
WACV23(4870-4880)
IEEE DOI 2302
Image segmentation, Computational modeling, Image retrieval, Bicycles, keywords instance-level retrieval, re-identification, transformers BibRef

Kamenou, E.[Eleni], del Rincon, J.M.[Jesus Martinez], Miller, P.[Paul], Devlin-Hill, P.[Patricia],
Closing the Domain Gap for Cross-modal Visible-Infrared Vehicle Re-identification,
ICPR22(2728-2734)
IEEE DOI 2212
Measurement, Visualization, Data visualization, Benchmark testing, Feature extraction, Sensors BibRef

Chen, H.[Haobo], Liu, Y.[Yang], Huang, Y.[Yang], Ke, W.[Wei], Sheng, H.[Hao],
Partition and Reunion: A Viewpoint-Aware Loss for Vehicle Re-Identification,
ICIP22(2246-2250)
IEEE DOI 2211
Benchmark testing, Task analysis, Vehicle re-identification, viewpoint-aware, loss function, representation learning BibRef

Li, M.[Manyu], Wei, M.[Mengwan], He, X.[Xin], Shen, F.[Fei],
Enhancing Part Features via Contrastive Attention Module for Vehicle Re-identification,
ICIP22(1816-1820)
IEEE DOI 2211
Part feature, contrastive attention, vehicle re-identification BibRef

Sommer, L.[Lars], Krüger, W.[Wolfgang],
Usage of Vehicle Re-Identification Models for Improved Persistent Multiple Object Tracking in Wide Area Motion Imagery,
ICIP22(331-335)
IEEE DOI 2211
Representation learning, Visualization, Surveillance, Object detection, Feature extraction, Multitasking, Data models, vehicle re-identification BibRef

Qian, W.[Wen], Luo, H.[Hao], Peng, S.[Silong], Wang, F.[Fan], Chen, C.[Chen], Li, H.[Hao],
Unstructured Feature Decoupling for Vehicle Re-identification,
ECCV22(XIV:336-353).
Springer DOI 2211
BibRef

Li, F.[Fei], Wang, Z.[Zhen], Nie, D.[Ding], Zhang, S.Y.[Shi-Yi], Jiang, X.Q.[Xing-Qun], Zhao, X.X.[Xing-Xing], Hu, P.[Peng],
Multi-Camera Vehicle Tracking System for AI City Challenge 2022,
AICity22(3264-3272)
IEEE DOI 2210
Vehicle detection, Urban areas, Merging, Predictive models, Feature extraction BibRef

Yao, H.[Hui], Duan, Z.[Zhizhao], Xie, Z.[Zhen], Chen, J.B.[Jing-Bo], Wu, X.[Xi], Xu, D.[Duo], Gao, Y.[Yutao],
City-Scale Multi-Camera Vehicle Tracking based on Space-Time-Appearance Features,
AICity22(3309-3317)
IEEE DOI 2210
Interpolation, Target tracking, Object detection, Feature extraction, Cameras, Trajectory BibRef

Mehta, R.[Rajat], Kaechele, M.[Markus], Stricker, D.[Didier], Bajcinca, N.[Naim],
Cluster-based Convolutional Baseline for Multi-Camera Vehicle Re-identification,
CIAP22(II:541-552).
Springer DOI 2205
BibRef

He, S.T.[Shu-Ting], Luo, H.[Hao], Wang, P.[Pichao], Wang, F.[Fan], Li, H.[Hao], Jiang, W.[Wei],
TransReID: Transformer-based Object Re-Identification,
ICCV21(14993-15002)
IEEE DOI 2203
Representation learning, Codes, Convolution, Neural networks, Benchmark testing, Transformers, Biometrics, Image and video retrieval BibRef

Zhao, J.J.[Jia-Jian], Zhao, Y.F.[Yi-Fan], Li, J.[Jia], Yan, K.[Ke], Tian, Y.H.[Yong-Hong],
Heterogeneous Relational Complement for Vehicle Re-identification,
ICCV21(205-214)
IEEE DOI 2203
Semantics, Benchmark testing, Position measurement, Cameras, Recognition and classification, Image and video retrieval BibRef

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

Li, M.[Ming], Huang, X.M.[Xin-Ming], Zhang, Z.M.[Zi-Ming],
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond,
ICCV21(194-204)
IEEE DOI 2203
Representation learning, Visualization, Annotations, Scalability, Benchmark testing, Task analysis, Recognition and classification, Image and video retrieval BibRef

Dou, X.Z.[Xin-Ze], Liu, Y.[Yang], Lv, K.[Kai], Xiong, Z.[Zhang], Sheng, H.[Hao],
High Confidence Attribute Recognition for Vehicle Re-Identification,
ICIP21(2353-2357)
IEEE DOI 2201
Image recognition, Feature extraction, Cameras, Videos, Vehicle re-identification, high confidence attributes, orientation learning BibRef

Besbes, M.D.E.[Mohamed Dhia Elhak], Tabia, H.[Hedi], Kessentini, Y.[Yousri], Hamed, B.B.[Bassem Ben],
Progressive Learning With Anchoring Regularization for Vehicle Re-Identification,
ICIP21(1154-1158)
IEEE DOI 2201
Training, Databases, Surveillance, Supervised learning, Training data, Feature extraction, Vehicle re-identification, anchoring regularization BibRef

Zhao, J.N.[Jia-Nan], Qi, F.L.[Feng-Liang], Ren, G.Y.[Guang-Yu], Xu, L.[Lin],
PhD Learning: Learning with Pompeiu-hausdorff Distances for Video-based Vehicle Re-Identification,
CVPR21(2225-2235)
IEEE DOI 2111
Learning systems, Visualization, Surveillance, Resists, Benchmark testing, Data models, Pattern recognition BibRef

Liu, C.[Chong], Zhang, Y.Q.[Yu-Qi], Luo, H.[Hao], Tang, J.[Jiasheng], Chen, W.H.[Wei-Hua], Xu, X.Z.[Xian-Zhe], 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.Y.[Min-Yue], Zhang, X.M.[Xuan-Meng], Yu, Y.[Yue], Bai, Z.C.[Ze-Chen], Zheng, Z.D.[Zhe-Dong], 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.Z.[Xian-Zhe], Gu, J.Y.[Jian-Yang], Zhang, Y.Q.[Yu-Qi], Liu, C.[Chong], Jiang, Y.Q.[Yi-Qi], 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.T.[Long-Tao], Tian, J.H.[Jia-Hao], Sun, G.Z.[Guang-Ze], Wang, G.[Gang], Cao, J.L.[Jun-Liang], Zhao, Z.F.[Zhi-Feng], 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.M.[Zhe-Ming], Wei, L.[Lili], Lang, C.Y.[Cong-Yan], Feng, S.H.[Song-He], 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

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