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
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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],
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Mei, T.[Tao],
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MultMed(20), No. 3, March 2018, pp. 645-658.
IEEE DOI
1802
BibRef
Earlier:
A Deep Learning-Based Approach to Progressive Vehicle Re-identification
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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],
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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
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Khan, S.D.[Sultan Daud],
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Elsevier DOI
1905
Survey, Vehicle Re-Identification. Re-identification, Hand-crafted methods,
Convolutional neural network, Traffic analysis
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Wu, F.Y.[Fang-Yu],
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Elsevier DOI
1906
Vehicle re-identification, Convolutional neural networks,
Semi-supervised learning, Re-ranking
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Lou, Y.H.[Yi-Hang],
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IP(28), No. 8, August 2019, pp. 3794-3807.
IEEE DOI
1907
embedded systems, image sampling,
learning (artificial intelligence), cross-view generation,
cross-view
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Guo, H.,
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Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle
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IEEE DOI
1908
cameras, feature extraction, learning (artificial intelligence),
object recognition, traffic engineering computing,
feature embedding
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ICIAR19(I:195-206).
Springer DOI
1909
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Kan, S.C.[Shi-Chao],
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Supervised Deep Feature Embedding With Handcrafted Feature,
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IEEE DOI
1909
Measurement, Image retrieval, Feature extraction, Fuses,
Task analysis, Training, Neural networks, Deep feature embedding,
vehicle re-identification
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Liu, X.,
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IP(29), 2020, pp. 2638-2652.
IEEE DOI
2001
Vehicle re-identification, CNN, global-regional feature learning,
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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
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Španhel, J.[Jakub],
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CVIU(192), 2020, pp. 102883.
Elsevier DOI
2002
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Zapletal, D.,
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Vehicle Re-identification for Automatic Video Traffic Surveillance,
Traffic16(1568-1574)
IEEE DOI
1612
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Tumrani, S.[Saifullah],
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Partial attention and multi-attribute learning for vehicle
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PRL(138), 2020, pp. 290-297.
Elsevier DOI
1806
Vehicle re-identification, Keypoint detection, Multi-branch network
BibRef
Wang, Y.F.[Yue-Feng],
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Wei, Y.[Ying],
Wang, C.Y.[Chu-Yuan],
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Vehicle re-identification based on unsupervised local area detection
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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
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],
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The Devil Is in the Details:
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ECCV20(XIV:369-386).
Springer DOI
2011
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Chen, T.S.[Tsai-Shien],
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Orientation-aware Vehicle Re-identification with Semantics-guided Part
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ECCV20(II:330-346).
Springer DOI
2011
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Zheng, Z.,
Jiang, M.,
Wang, Z.,
Wang, J.,
Bai, Z.,
Zhang, X.,
Yu, X.,
Tan, X.,
Yang, Y.,
Wen, S.,
Ding, E.,
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
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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
Moral, P.,
García-Martín, Á.,
Martínez, J.M.,
Vehicle Re-Identification in Multi-Camera scenarios based on
Ensembling Deep Learning Features,
City20(2574-2580)
IEEE DOI
2008
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
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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
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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
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Porrello, A.[Angelo],
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Robust Re-identification by Multiple Views Knowledge Distillation,
ECCV20(X:93-110).
Springer DOI
2011
Code, Re-Identification.
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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 .