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Cameras, Data models, Deep learning, Labeling, Adaptation models, Unsupervised learning, Training data, Person re-identification, multi-task deep learning BibRef

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Cameras, Feature extraction, Task analysis, Visualization, Video sequences, Aggregates, Bridges, Deep learning, video based person re-identification BibRef

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IEEE DOI 1902
Feature extraction, Measurement, Cameras, Benchmark testing, Probes, Image color analysis, Histograms, Person re-identification, benchmark BibRef

Zheng, M., Karanam, S.[Srikrishna], Radke, R.J.[Richard J.],
RPIfield: A New Dataset for Temporally Evaluating Person Re-identification,
WiCV18(1974-19742)
IEEE DOI 1812
Cameras, Probes, Benchmark testing, Measurement, Economic indicators, Legged locomotion BibRef

Narayan, N., Sankaran, N., Setlur, S., Govindaraju, V.,
Re-identification for Online Person Tracking by Modeling Space-Time Continuum,
Joint18(1519-151909)
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Person re-identification BibRef

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Person, re-identification BibRef

Yin, J.H.[Jia-Hang], Wu, A.[Ancong], Zheng, W.S.[Wei-Shi],
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Yang, Q.[Qize], Wu, A.[Ancong], Zheng, W.S.[Wei-Shi],
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IEEE DOI 2106
Clothing, Cameras, Feature extraction, Image color analysis, Shape, Reliability, Visualization, Person re-identification, clothing change BibRef

Zhu, X., Wu, B., Huang, D., Zheng, W.S.,
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IEEE DOI 1804
image matching, image representation, learning (artificial intelligence), open search space BibRef

Zhang, Z.Y.[Zong-Yan], Zhao, C.R.[Cai-Rong], Miao, D.Q.[Duo-Qian], Wang, X.[Xuekuan], Lai, Z.H.[Zhi-Hui], Yang, J.[Jian],
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Wu, L.[Lin], Wang, Y.[Yang], Gao, J.B.[Jun-Bin], Li, X.[Xue],
Deep adaptive feature embedding with local sample distributions for person re-identification,
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Wu, L.[Lin], Wang, Y.[Yang], Ge, Z.Y.[Zong-Yuan], Hu, Q.C.[Qi-Chang], Li, X.[Xue],
Structured deep hashing with convolutional neural networks for fast person re-identification,
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Person re-identification, Convolutional neural networks, Deep hashing, Structured embedding BibRef

Wu, L.[Lin], Wang, Y.[Yang], Li, X.[Xue], Gao, J.B.[Jun-Bin],
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Li, S.Q.[Shuang-Qun], Ma, H.D.[Hua-Dong],
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Zhao, Z.C.[Zhi-Cheng], Zhao, B.L.[Bin-Lin], Su, F.[Fei],
Person re-identification via integrating patch-based metric learning and local salience learning,
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Elsevier DOI 1712
Person re-identification BibRef

Ren, Y.T.[Yu-Tao], Li, X.L.[Xue-Long], Lu, X.Q.[Xiao-Qiang],
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Person re-identification BibRef

Jiang, Z.Q.[Zheng-Qiang], Huynh, D.Q.[Du Q.],
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Adaptation models, Computational modeling, Histograms, Image color analysis, Target tracking, Videos, Munkres' algorithm, visual tracking BibRef

Jiang, Z.Q.[Zheng-Qiang], Huynh, D.Q.[Du Q.], Moran, W.[William], Challa, S.[Subhash], Spadaccini, N.[Nick],
Multiple Pedestrian Tracking Using Colour and Motion Models,
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Jiang, Z.Q.[Zheng-Qiang], Huynh, D.Q.[Du Q.], Moran, W.[William], Challa, S.[Subhash],
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Zhou, S., Wang, J., Shi, R., Hou, Q., Gong, Y., Zheng, N.,
Large Margin Learning in Set-to-Set Similarity Comparison for Person Reidentification,
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IEEE DOI 1802
Cameras, Feature extraction, Learning systems, Machine learning, Measurement, Neural networks, Robustness, Person re-identification, set to set similarity comparison BibRef

Lejbølle, A.R.[Aske R.], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.],
Enhancing person re-identification by late fusion of low-, mid- and high-level features,
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Looking beyond appearances: Synthetic training data for deep CNNs in re-identification,
CVIU(167), 2018, pp. 50-62.
Elsevier DOI 1804
Re-identification, Deep learning, Training set, Automated training dataset generation, Re-identification photorealistic dataset BibRef

Tian, Y.[Ying], Zeng, M.Y.[Ming-Yong], Lu, A.[AiHong], Gao, B.[Bin], Luo, Z.K.[Zhang-Kai],
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Feng, Z., Lai, J., Xie, X.,
Learning View-Specific Deep Networks for Person Re-Identification,
IP(27), No. 7, July 2018, pp. 3472-3483.
IEEE DOI 1805
Benchmark testing, Cameras, Computational modeling, Dictionaries, Feature extraction, Machine learning, Measurement, view-specific deep networks BibRef

Chen, Y.[Ying], Yuan, J.[Jin], Li, Z.Y.[Zhi-Yong], Wu, Y.Q.[Yi-Qiang], Nouioua, M.[Mourad], Xie, G.[Guoqi],
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Person re-identification, Re-ranking, Expanded k-reciprocal neighbors, Rank list similarity BibRef

Lv, J.Y.[Jing-Yi], Li, Z.Y.[Zhi-Yong], Nai, K.[Ke], Chen, Y.[Ying], Yuan, J.[Jin],
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Person re-identification, Re-ranking, Expanded neighborhoods distance, Two-level neighborhoods BibRef

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Person re-identification, Part-level feature, Prediction alignment, Global-local feature BibRef

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Elsevier DOI 1904
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ICCV17(2468-2477)
IEEE DOI 1802
Group Re-Identification, Dictionary learning, Encoding. feature extraction, group theory, image coding, image matching, image representation, image sensors, unsupervised learning, Re, Visualization BibRef

Martinel, N.[Niki], Foresti, G.L.[Gian Luca], Micheloni, C.[Christian],
Distributed person re-identification through network-wise rank fusion consensus,
PRL(124), 2019, pp. 63-73.
Elsevier DOI 1906
Re-Identification, Distributed, Camera network, Rank fusion, Consensus BibRef

Lu, J., He, Y., Liu, T., Chen, X.,
Centralized and Clustered Features for Person Re-Identification,
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IEEE DOI 1906
Feature extraction, Training, Reliability, Signal processing algorithms, Unsupervised learning, penalty term BibRef

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IEEE DOI 1908
Measurement, Feature extraction, Semantics, Image color analysis, Reliability, Probes, Topology, Person re-identification, topological relationship BibRef

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History, Cameras, Task analysis, Optimization, Trajectory, Video surveillance, Image retrieval, Person re-identification, log, cross-ranking BibRef

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Feature extraction, Surveillance, Lighting, Task analysis, Training, Convergence, Measurement, Disentanglement, surveillance BibRef

Zeng, Z., Wang, Z., Wang, Z., Zheng, Y., Chuang, Y.Y., Satoh, S.,
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Lighting, Cameras, Task analysis, Training, Feature extraction, Testing, Generators, Person re-identification, feature disentanglement BibRef

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ICIP19(1212-1216)
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contextual similarity, graph-based similarity, re-ranking, diffusion, person re-identification BibRef

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Person Search, Person Re-Identification, Multi-Scale BibRef

Zhang, Y.S.[Yu-Sheng], Zhou, Z.H.[Zhi-Heng], Li, B.[Bo], Huang, Y.[Yu], Huang, J.C.[Jun-Chu], Chen, Z.Q.[Zeng-Qun],
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Elsevier DOI 1912
Person re-identification, Feature representation, Multi-level feature, Part-based feature, Grafting BibRef

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Person re-identification, Re-ranking, Similarity metric BibRef

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IEEE DOI 1912
Measurement, Task analysis, Robustness, Learning systems, Visualization, Benchmark testing, Computational modeling, Fusion, person reidentification (person re-id) BibRef

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Feature extraction, Legged locomotion, Semantics, Optical computing, Optical imaging, Visualization, multi-model ensemble BibRef

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Deep feature, Hand-crafted, Multimodal feature fusion, Person re-identification BibRef

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Feature extraction, Training, Optimization, Task analysis, Computational modeling, Data mining, Semantics, image retrieval. BibRef

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Zhang, Z.Z.[Zhi-Zheng], Lan, C.L.[Cui-Ling], Zeng, W.J.[Wen-Jun], Jin, X.[Xin], Chen, Z.B.[Zhi-Bo],
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CVPR20(3183-3192)
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Zhou, J.M.[Jie-Ming], Roy, S.K.[Soumava Kumar], Fang, P.F.[Peng-Fei], Harandi, M.[Mehrtash], Petersson, L.[Lars],
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Feature extraction, Training, Measurement, Robustness, Machine learning, Probes, Task analysis, Person re-identification, quadruplet BibRef

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Hou, R.B.[Rui-Bing], Chang, H.[Hong], Ma, B.P.[Bing-Peng], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
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Elsevier DOI 1806
Person re-identification, Automated surveillance, People analysis, Smart city applications BibRef

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Yao, H.T.[Han-Tao], Xu, C.S.[Chang-Sheng],
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Probes, Search problems, Detectors, Proposals, Visualization, Noise measurement, Transforms, Detection-Matching person search, person re-identification BibRef

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Correlation, Task analysis, Optimization, Training, Learning systems, Semantics, Lighting, Person re-identification, max pooling BibRef

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Person Re-identification, Body misalignment, Foreground features, Background information BibRef

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IEEE DOI 2101
Legged locomotion, Lasers, Target tracking, Robot sensing systems, Indoor environments, Human following, indoor environments, mobile robot BibRef

Lin, W., Li, Y., Xiao, H., See, J., Zou, J., Xiong, H., Wang, J., Mei, T.,
Group Reidentification with Multigrained Matching and Integration,
Cyber(51), No. 3, March 2021, pp. 1478-1492.
IEEE DOI 2102
Layout, Feature extraction, Cameras, Task analysis, Reliability, Probes, Heuristic algorithms, Group reidentification (Re-ID), Re-ID BibRef

Li, P., Pan, P., Liu, P., Xu, M., Yang, Y.,
Hierarchical Temporal Modeling With Mutual Distance Matching for Video Based Person Re-Identification,
CirSysVideo(31), No. 2, February 2021, pp. 503-511.
IEEE DOI 2102
Feature extraction, Video sequences, Task analysis, Convolution, Probes, Distance measurement, Training, probe-gallery mutual distance BibRef

Yaghoubi, E.[Ehsan], Kumar, A.[Aruna], Proença, H.[Hugo],
SSS-PR: A short survey of surveys in person re-identification,
PRL(143), 2021, pp. 50-57.
Elsevier DOI 2102
Survey, Re-Identification. Person re-identification, Privacy and security, Visual surveillance BibRef

Yaghoubi, E.[Ehsan], Borza, D.[Diana], Aruna Kumar, S.V., Proença, H.[Hugo],
Person re-identification: Implicitly defining the receptive fields of deep learning classification frameworks,
PRL(145), 2021, pp. 23-29.
Elsevier DOI 2104
Person re-identification, Data augmentation, Explicit attention mechanism, Visual surveillance BibRef

Han, K.[Ke], Huang, Y.[Yan], Song, C.F.[Chun-Feng], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Adaptive super-resolution for person re-identification with low-resolution images,
PR(114), 2021, pp. 107682.
Elsevier DOI 2103
Person re-identification, Super-resolution, Body regions, Adaptive feature integration BibRef

Han, K.[Ke], Huang, Y.[Yan], Chen, Z.[Zerui], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Prediction and Recovery for Adaptive Low-resolution Person Re-identification,
ECCV20(XXVI:193-209).
Springer DOI 2011
BibRef

Wang, Y.J.[Yong-Jie], Zhang, W.[Wei], Liu, Y.Y.[Yan-Yan],
Multi-scale feature fusion network for person re-identification,
IET-IPR(14), No. 17, 24 December 2020, pp. 4614-4620.
DOI Link 2104
BibRef

Wang, Y.J.[Yong-Jie], Zhang, W.[Wei], Huang, D.X.[Dong-Xiao], Liu, Y.Y.[Yan-Yan],
Multi-level feature fusion and multi-loss learning for person Re-Identification,
SP:IC(94), 2021, pp. 116197.
Elsevier DOI 2104
Self-attention module, Relative weight, Multi-loss learning BibRef

Sun, J.[Jia], Li, Y.F.[Yan-Feng], Chen, H.J.[Hou-Jin], Zhang, B.[Bin], Zhu, J.L.[Jin-Lei],
MEMF: Multi-level-attention embedding and multi-layer-feature fusion model for person re-identification,
PR(116), 2021, pp. 107937.
Elsevier DOI 2106
Person re-identification, Feature expression, Convolutional neural network BibRef

Wang, H.X.[Hong-Xia], Chen, X.[Xiang], Liu, C.[Chun],
Pose-guided part matching network via shrinking and reweighting for occluded person re-identification,
IVC(111), 2021, pp. 104186.
Elsevier DOI 2106
Person re-identification, Pose estimation, Graph matching, Soft thresholding BibRef

Zhang, X.K.[Xiao-Kang], Yan, Y.[Yan], Xue, J.H.[Jing-Hao], Hua, Y.[Yang], Wang, H.Z.[Han-Zi],
Semantic-Aware Occlusion-Robust Network for Occluded Person Re-Identification,
CirSysVideo(31), No. 7, July 2021, pp. 2764-2778.
IEEE DOI 2107
Feature extraction, Semantics, Task analysis, Pose estimation, Image segmentation, Clutter, Cameras, Person re-identification, multi-task learning BibRef

Zhu, J.[Ji], Yang, H.[Hua], Lin, W.Y.[Wei-Yao], Liu, N.[Nian], Wang, J.[Jia], Zhang, W.J.[Wen-Jun],
Group Re-Identification With Group Context Graph Neural Networks,
MultMed(23), 2021, pp. 2614-2626.
IEEE DOI 2109
Layout, Feature extraction, Cameras, Task analysis, Kernel, Training, Measurement, Group re-identification, spatial K-NN graph, group context graph neural network BibRef

Zheng, Y.[Yu], Velipasalar, S.[Senem],
Part-Based Feature Squeezing To Detect Adversarial Examples in Person Re-Identification Networks,
ICIP21(844-848)
IEEE DOI 2201
Deep learning, Image segmentation, Perturbation methods, Object detection, Feature extraction, Person re-identification, DNN BibRef

Coleiro, A.[Andre], Scerri, D.[Daren],
Security Automation Through a Multi-processing Real-Time System for the Re-Identification of Persons,
ISVC21(II:141-153).
Springer DOI 2112
BibRef

Gilroy, S.[Shane], Glavin, M.[Martin], Jones, E.[Edward], Mullins, D.[Darragh],
Pedestrian Occlusion Level Classification using Keypoint Detection and 2D Body Surface Area Estimation,
OVIS21(3826-3832)
IEEE DOI 2112
Annotations, Image edge detection, Semantics, Estimation, Benchmark testing BibRef

Pu, N.[Nan], Chen, W.[Wei], Liu, Y.[Yu], Bakker, E.M.[Erwin M.], Lew, M.S.[Michael S.],
Lifelong Person Re-Identification via Adaptive Knowledge Accumulation,
CVPR21(7897-7906)
IEEE DOI 2111
Training, Visualization, Adaptation models, Codes, Cognitive processes, Knowledge representation BibRef

Choi, S.[Seokeon], Kim, T.[Taekyung], Jeong, M.[Minki], Park, H.[Hyoungseob], Kim, C.[Changick],
Meta Batch-Instance Normalization for Generalizable Person Re-Identification,
CVPR21(3424-3434)
IEEE DOI 2111
Codes, Computational modeling, Pipelines, Benchmark testing, Data models, Pattern recognition BibRef

Zhang, X.[Xiao], Ge, Y.X.[Yi-Xiao], Qiao, Y.[Yu], Li, H.S.[Hong-Sheng],
Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification,
CVPR21(3435-3444)
IEEE DOI 2111
Training, Annotations, Refining, Pattern recognition, Noise measurement BibRef

Zhang, A.[Anguo], Gao, Y.M.[Yue-Ming], Niu, Y.[Yuzhen], Liu, W.X.[Wen-Xi], Zhou, Y.[Yongcheng],
Coarse-to-Fine Person Re-Identification with Auxiliary-Domain Classification and Second-Order Information Bottleneck,
CVPR21(598-608)
IEEE DOI 2111
Image coding, Surveillance, Redundancy, Neural networks, Feature extraction, Cameras BibRef

Kraus, M.[Maximilian], Azimi, S.M.[Seyed Majid], Ercelik, E.[Emec], Bahmanyar, R.[Reza], Reinartz, P.[Peter], Knoll, A.[Alois],
AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features,
ICPR21(2454-2461)
IEEE DOI 2105
Image quality, Tracking, Atmospheric modeling, Neural networks, Predictive models, Prediction algorithms, Trajectory BibRef

Zheng, C.[Chong], Wei, P.[Ping], Zheng, N.N.[Nan-Ning],
A Duplex Spatiotemporal Filtering Network for Video-based Person Re-identification,
ICPR21(7551-7557)
IEEE DOI 2105
Surveillance, Video sequences, Semantics, Benchmark testing, Information filters, Feature extraction, Spatiotemporal phenomena BibRef

Zhao, C.[Chao], Zhang, Z.Y.[Zhen-Yu], Yan, J.[Jian], Yan, Y.[Yan],
Decoupled Self-attention Module for Person Re-identification,
ICPR21(7617-7624)
IEEE DOI 2105
Correlation, Semantics, Lighting, Information filters, Cameras, Robustness BibRef

Ni, X.Y.[Xing-Yang], Fang, L.[Liang], Huttunen, H.[Heikki],
Adaptive L2 Regularization in Person Re-Identification,
ICPR21(9601-9607)
IEEE DOI 2105
Training, Backpropagation, Adaptation models, Adaptive systems, Neural networks, Image retrieval, Object detection BibRef

Ai, M.J.[Ming-Jing], Shan, G.Z.[Guo-Zhi], Liu, B.[Bo], Liu, T.Y.[Tian-Yang],
Rethinking ReID: Multi-Feature Fusion Person Re-identification Based on Orientation Constraints,
ICPR21(1904-1911)
IEEE DOI 2105
Training, Computational modeling, Focusing, Pattern recognition, Security, Videos, person re-identification, orientation classifier BibRef

Ding, W.J.[Wen-Jie], Wei, X.[Xing], Ji, R.R.[Rong-Rong], Hong, X.P.[Xiao-Peng], Gong, Y.H.[Yi-Hong],
Polynomial Universal Adversarial Perturbations for Person Re-Identification,
ICPR21(1144-1151)
IEEE DOI 2105
Correlation coefficient, Additives, Perturbation methods, Modulation, Pattern recognition BibRef

Han, C., Gao, C., Sang, N.,
Keypoint-Based Feature Matching For Partial Person Re-Identification,
ICIP20(226-230)
IEEE DOI 2011
Feature extraction, Cameras, Task analysis, Pose estimation, Training, Pipelines, Generative adversarial networks, Partial, Re-ID BibRef

Liu, M., Dai, Y., Wu, S., Bai, Y., Duan, L.Y.,
Extending Hashing Towards Fast Re-Identification,
ICIP20(1551-1555)
IEEE DOI 2011
Training, Binary codes, Protocols, Convergence, Visualization, Quantization (signal), Entropy, Hashing, Re-Identification, Pooling BibRef

Wang, G.[Guan'an], Gong, S.G.[Shao-Gang], Cheng, J.[Jian], Hou, Z.G.[Zeng-Guang],
Faster Person Re-identification,
ECCV20(VIII:275-292).
Springer DOI 2011
BibRef

Zhao, S.Z.[Shi-Zhen], Gao, C.X.[Chang-Xin], Zhang, J.[Jun], Cheng, H.[Hao], Han, C.[Chuchu], Jiang, X.Y.[Xin-Yang], Guo, X.W.[Xiao-Wei], Zheng, W.S.[Wei-Shi], Sang, N.[Nong], Sun, X.[Xing],
Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians,
ECCV20(VI:647-663).
Springer DOI 2011
BibRef

Song, X.L.[Xiao-Lin], Zhao, K.[Kaili], Chu, W.S.[Wen-Sheng], Zhang, H.G.[Hong-Gang], Guo, J.[Jun],
Progressive Refinement Network for Occluded Pedestrian Detection,
ECCV20(XXIII:32-48).
Springer DOI 2011
BibRef

Li, M., Xu, H., Wang, J., Li, W., Sun, Y.,
Temporal Aggregation with Clip-level Attention for Video-based Person Re-identification,
WACV20(3365-3373)
IEEE DOI 2006
Feature extraction, Training, Task analysis, Measurement, Robustness, Computational modeling, Aggregates BibRef

Lu, R., Ma, H.,
Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor,
ICIP19(1640-1644)
IEEE DOI 1910
Occluded pedestrian detection, visible ratio, box sign predictor, localization accuracy BibRef

Ghorbel, M.[Mahmoud], Ammar, S.[Sourour], Kessentini, Y.[Yousri], Jmaiel, M.[Mohamed],
Improving Person Re-identification by Background Subtraction Using Two-Stream Convolutional Networks,
ICIAR19(I:345-356).
Springer DOI 1909
BibRef

Zamprogno, M.[Marco], Passon, M.[Marco], Martinel, N.[Niki], Serra, G.[Giuseppe], Lancioni, G.[Giuseppe], Micheloni, C.[Christian], Tasso, C.[Carlo], Foresti, G.L.[Gian Luca],
Video-Based Convolutional Attention for Person Re-Identification,
CIAP19(I:3-14).
Springer DOI 1909
BibRef

Li, W.[Wenbo], Chen, Z.[Ze], Fu, Z.Y.[Zhen-Yong], Lu, H.T.[Hong-Tao],
Multilevel Collaborative Attention Network for Person Search,
ACCV18(I:467-482).
Springer DOI 1906
pedestrian detection and person re-identification simultaneously. BibRef

Ristani, E., Tomasi, C.,
Features for Multi-target Multi-camera Tracking and Re-identification,
CVPR18(6036-6046)
IEEE DOI 1812
Correlation, Cameras, Trajectory, Feature extraction, Training, Detectors, Benchmark testing BibRef

Babaee, M., Li, Z., Rigoll, G.,
Occlusion Handling in Tracking Multiple People Using RNN,
ICIP18(2715-2719)
IEEE DOI 1809
Target tracking, Training, Legged locomotion, Trajectory, Video sequences, Tracking, Motion, Occlusion, RNN, Deep learning BibRef

Huang, Z.X.[Zeng-Xi], Feng, Z.H.[Zhen-Hua], Yan, F.[Fei], Kittler, J.V.[Josef V.], Wu, X.J.[Xiao-Jun],
Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset,
AMDO18(63-72).
Springer DOI 1807
BibRef

Zhang, P.[Peng], Wu, Q.[Qiang], Xu, J.S.[Jing-Song], Zhang, J.[Jian],
Long-Term Person Re-identification Using True Motion from Videos,
WACV18(494-502)
IEEE DOI 1806
feature extraction, image coding, image matching, image motion analysis, image representation, image sequences, Videos BibRef

Wojke, N., Bewley, A.,
Deep Cosine Metric Learning for Person Re-identification,
WACV18(748-756)
IEEE DOI 1806
feature extraction, image classification, learning (artificial intelligence), query processing, vectors, Trajectory BibRef

Ustinova, E., Ganin, Y., Lempitsky, V.,
Multi-Region bilinear convolutional neural networks for person re-identification,
AVSS17(1-6)
IEEE DOI 1806
convolution, image classification, image matching, neural nets, bilinear pooling, bilinear-CNN architecture, Streaming media BibRef

Zhang, K.X.[Kai-Xuan], Xu, Y.[Yang], Sun, L.[Li], Qiu, S.[Song], Li, Q.L.[Qing-Li],
Person Re-id by Incorporating PCA Loss in CNN,
MMMod18(II:200-212).
Springer DOI 1802
BibRef

Peralta, B.[Billy], Caro, L.[Luis], Soto, A.[Alvaro],
Unsupervised Local Regressive Attributes for Pedestrian Re-identification,
CIARP17(517-524).
Springer DOI 1802
BibRef
Earlier: A1, A3, Only:
Multi-target Tracking with Sparse Group Features and Position Using Discrete-Continuous Optimization,
HIS14(680-694).
Springer DOI 1504
BibRef

Chahar, H.[Harendra], Nain, N.[Neeta],
A Study on Deep Convolutional Neural Network Based Approaches for Person Re-identification,
PReMI17(543-548).
Springer DOI 1711
BibRef

Xu, B.[Bolei], Qiu, G.P.[Guo-Ping],
Unsupervised Person Re-identification via Graph-Structured Image Matching,
HIS16(III: 301-314).
Springer DOI 1704
BibRef

Huynh, X.P.[Xuan-Phung], Choi, I.H.[In-Ho], Kim, Y.G.[Yong-Guk],
Tracking a Human Fast and Reliably Against Occlusion and Human-Crossing,
PSIVT15(461-472).
Springer DOI 1602
BibRef

Zhou, Z.[Zhi], Wang, Y.[Yue], Teoh, E.K.[Eam Khwang],
Double layer salient parts based multi-people tracking,
ICIP15(3067-3071)
IEEE DOI 1512
Multi-people tracking BibRef

de Carvalho Prates, R.F.[Raphael Felipe], Schwartz, W.R.[William Robson],
CBRA: Color-based ranking aggregation for person re-identification,
ICIP15(1975-1979)
IEEE DOI 1512
color features BibRef

Si, J.L.[Jian-Lou], Zhang, H.G.[Hong-Gang], Li, C.G.[Chun-Guang],
Regularization in metric learning for person re-identification,
ICIP15(2309-2313)
IEEE DOI 1512
Metric Learning; Person Re-identification; Regularization BibRef

Huang, S.[Shuai], Gu, Y.[Yun], Yang, J.[Jie], Shi, P.F.[Peng-Fei],
Reranking of person re-identification by manifold-based approach,
ICIP15(4253-4257)
IEEE DOI 1512
manifold; re-identification; reranking BibRef

Tsai, M.C.[Ming-Chia], Wei, C.P.[Chia-Po], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Graph regularized low-rank matrix recovery for robust person re-identification,
ICIP15(4654-4658)
IEEE DOI 1512
Graph Regularization; Low-Rank Matrix Recovery; Person Re-Identification BibRef

Fagot-Bouquet, L.[Loïc], Audigier, R.[Romaric], Dhome, Y.[Yoann], Lerasle, F.[Frédéric],
Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking,
ECCV16(VIII: 774-790).
Springer DOI 1611
BibRef
And:
Online multi-person tracking based on global sparse collaborative representations,
ICIP15(2414-2418)
IEEE DOI 1512
BibRef
Earlier:
Collaboration and spatialization for an efficient multi-person tracking via sparse representations,
AVSS15(1-6)
IEEE DOI 1511
Online tracking. computer vision BibRef

Ristani, E.[Ergys], Tomasi, C.[Carlo],
Tracking Multiple People Online and in Real Time,
ACCV14(V: 444-459).
Springer DOI 1504
BibRef

Mansouri, N., Ben Jemaa, Y., Motamed, C., Pinti, A., Watelain, E.,
A new strategy based on spatiogram similarity association for multi-pedestrian tracking,
IPTA14(1-6)
IEEE DOI 1503
image sequences BibRef

Chen, J.H.[Jia-Hui], Sheng, H.[Hao], Zhang, Y.[Yang], Xiong, Z.[Zhang],
Enhancing Detection Model for Multiple Hypothesis Tracking,
PETS17(2143-2152)
IEEE DOI 1709
Analytical models, Cameras, Correlation, Detectors, Target tracking, Trajectory BibRef

Sheng, H.[Hao], Liu, S.[Shukai], Ji, H.S.[Heng-Shan], Chen, J.H.[Jia-Hui], Xiong, Z.[Zhang],
A Pedestrian-Pedestrian and Pedestrian-Vehicle Interaction Motion Model for Pedestrians Tracking,
ISVC14(I: 270-280).
Springer DOI 1501
BibRef

Pham, V.Q.[Viet-Quoc], Kozakaya, T.[Tatsuo], Okada, R.[Ryuzo],
DIET: Dynamic Integration of Extended Tracklets for Tracking Multiple Persons,
ICPR14(1206-1211)
IEEE DOI 1412
Cities and towns BibRef

Yan, X.[Xu], Cheriyadat, A.[Anil], Shah, S.K.[Shishir K.],
Hierarchical Group Structures in Multi-person Tracking,
ICPR14(2221-2226)
IEEE DOI 1412
Dynamics BibRef

Gudys, A.[Adam], Rosner, J.[Jakub], Segen, J.[Jakub], Wojciechowski, K.[Konrad], Kulbacki, M.[Marek],
Tracking People in Video Sequences by Clustering Feature Motion Paths,
ICCVG14(236-245).
Springer DOI 1410
BibRef

Henschel, R.[Roberto], Leal-Taixé, L.[Laura], Rosenhahn, B.[Bodo],
Solving Multiple People Tracking in a Minimum Cost Arborescence,
MTT15(71-72)
IEEE DOI 1503
BibRef
Earlier:
Efficient Multiple People Tracking Using Minimum Cost Arborescences,
GCPR14(265-276).
Springer DOI 1411
BibRef

Xiang, Y., Alahi, A.[Alexandre], Savarese, S.[Silvio],
Learning to Track: Online Multi-object Tracking by Decision Making,
ICCV15(4705-4713)
IEEE DOI 1602
Decision making BibRef

Leal-Taixe, L.[Laura], Fenzi, M.[Michele], Kuznetsova, A.[Alina], Rosenhahn, B.[Bodo], Savarese, S.[Silvio],
Learning an Image-Based Motion Context for Multiple People Tracking,
CVPR14(3542-3549)
IEEE DOI 1409
image-based motion context BibRef

Wang, B.[Bing], Wang, G.[Gang], Chan, K.L.[Kap Luk], Wang, L.[Li],
Tracklet Association in Detect-Then-Track Paradigm for Long-Term Multi-person Tracking,
LTDT14(716-717)
IEEE DOI 1409
BibRef

Zhang, J.M.[Jian-Ming], lo Presti, L.[Liliana], Sclaroff, S.[Stan],
Online Multi-person Tracking by Tracker Hierarchy,
AVSS12(379-385).
IEEE DOI 1211
BibRef

Nie, W.Z.[Wei-Zhi], Liu, A.A.[An-An], Su, Y.T.[Yu-Ting],
Multiple Person Tracking by Spatiotemporal Tracklet Association,
AVSS12(481-486).
IEEE DOI 1211
BibRef

Jiang, X.Y.[Xiao-Yan], Rodner, E.[Erik], Denzler, J.[Joachim],
Multi-person Tracking-by-Detection Based on Calibrated Multi-camera Systems,
ICCVG12(743-751).
Springer DOI 1210
BibRef

Piatkowska, E.[Ewa], Kogler, J., Belbachir, A.N.[Ahmed Nabil], Gelautz, M.[Margrit],
Improved Cooperative Stereo Matching for Dynamic Vision Sensors with Ground Truth Evaluation,
ECVW17(370-377)
IEEE DOI 1709
BibRef
Earlier: A1, A3, A4, Only:
Asynchronous Stereo Vision for Event-Driven Dynamic Stereo Sensor Using an Adaptive Cooperative Approach,
CDC4CV13(45-50)
IEEE DOI 1403
Biosensors, Cameras, Heuristic algorithms, Voltage control. BibRef

Piatkowska, E.[Ewa], Belbachir, A.N.[Ahmed Nabil], Schraml, S.[Stephan], Gelautz, M.[Margrit],
Spatiotemporal multiple persons tracking using Dynamic Vision Sensor,
ECVW12(35-40).
IEEE DOI 1207
BibRef

Heili, A.[Alexandre], Odobez, J.M.[Jean-Marc],
Parameter estimation and contextual adaptation for a multi-object tracking CRF model,
PETS13(14-21)
IEEE DOI 1411
object detection BibRef

Heili, A.[Alexandre], Chen, C.[Cheng], Odobez, J.M.[Jean-Marc],
Detection-based multi-human tracking using a CRF model,
VS11(1673-1680).
IEEE DOI 1201
BibRef

Luo, X.H.[Xing-Han], Tan, R.T.[Robby T.], Veltkamp, R.C.[Remco C.],
Multi-person tracking based on vertical reference lines and dynamic visibility analysis,
ICIP11(1877-1880).
IEEE DOI 1201
BibRef

Thaler, M.[Marcus], Bailer, W.[Werner],
Real-Time Person Detection and Tracking in Panoramic Video,
CVSports13(1027-1032)
IEEE DOI 1309
broadcast; detection; panoramic; sports; tracking BibRef

Thaler, M.[Marcus], Kaiser, R.[Rene], Bailer, W.[Werner], Kriechbaum, A.[Andreas],
Tracking Persons in Ultra-HD Panoramic Video,
MMMod12(633-635).
Springer DOI 1201
BibRef

Yeh, H.H.[Hsin-Ho], Chen, J.Y.[Jiun-Yu], Huang, C.R.[Chun-Rong], Chen, C.S.[Chu-Song],
An adaptive approach for overlapping people tracking based on foreground silhouettes,
ICIP10(3489-3492).
IEEE DOI 1009
BibRef

Galoogahi, H.K.,
Tracking Groups of People in Presence of Occlusion,
PSIVT10(438-443).
IEEE DOI 1011
BibRef

Zen, G.[Gloria], Lanz, O.[Oswald], Messelodi, S.[Stefano], Ricci, E.[Elisa],
Tracking Multiple People with Illumination Maps,
ICPR10(3484-3487).
IEEE DOI 1008
BibRef

Bütepage, J., Black, M.J., Kragic, D.[Danica], Kjellström, H.[Hedvig],
Deep Representation Learning for Human Motion Prediction and Classification,
CVPR17(1591-1599)
IEEE DOI 1711
Computational modeling, Correlation, Decoding, Encoding, Feature extraction, Hidden, Markov, models BibRef

Kjellstrom, H.[Hedvig], Kragic, D.[Danica], Black, M.J.[Michael J.],
Tracking people interacting with objects,
CVPR10(747-754).
IEEE DOI 1006
BibRef

Breitenstein, M.D.[Michael D.], Reichlin, F.[Fabian], Leibe, B.[Bastian], Koller-Meier, E.[Esther], Van Gool, L.J.[Luc J.],
Robust tracking-by-detection using a detector confidence particle filter,
ICCV09(1515-1522).
IEEE DOI 0909
multi-person tracking. Particle filter framework. BibRef

Ishiguro, K.[Katsuhiko], Yamada, T.[Takeshi], Ueda, N.[Naonori],
Simultaneous clustering and tracking unknown number of objects,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Apewokin, S., Valentine, B., Bales, M.R.[M. Ryan], Wills, L.M.[Linda M.], Wills, D.S.[D. Scott],
Tracking multiple pedestrians in real-time using kinematics,
EmbedCV08(1-6).
IEEE DOI 0806
BibRef

Boufama, B., Ali, M.A.,
Tracking Multiple People in the Context of Video Surveillance,
ICIAR07(581-592).
Springer DOI 0708
BibRef

Feldmann, T.[Tobias], Scheuermann, B.[Björn], Rosenhahn, B.[Bodo], Wörner, A.[Annika],
N-View Human Silhouette Segmentation in Cluttered, Partially Changing Environments,
DAGM10(363-372).
Springer DOI 1009
BibRef

Rosenhahn, B.[Bodo], Klette, R.[Reinhard], Sommer, G.[Gerald],
Silhouette Based Human Motion Estimation,
DAGM04(294-301).
Springer DOI 0505
BibRef

Girondel, V., Caplier, A., Bonnaud, L.,
Real time tracking of multiple persons by Kalman filtering and face pursuit for multimedia applications,
Southwest04(201-205).
IEEE DOI 0411
BibRef

Mori, T.[Taketoshi], Matsumoto, T.[Takashi], Shimosaka, M.[Masamichi], Noguchi, H.[Hiroshi], Sato, T.[Tomomasa],
Multiple Persons Tracking with Data Fusion of Multiple Cameras and Sensing Floor Using Particle Filters,
M2SFA208(xx-yy). 0810
BibRef

Harada, T.[Tatsuya], Sato, T.[Tomomasa], Mori, T.[Taketoshi],
Human Motion Tracking System Based on Skeleton and Surface Integration Model Using Pressure Sensors Distribtuion Bed,
HUMO00(99-106).
IEEE Top Reference. 0010
BibRef

Roh, H., Kang, S., Lee, S.W.,
Multiple People Tracking Using Appearance Model Based on Temporal Color,
ICPR00(Vol IV: 643-646).
IEEE DOI 0009
BibRef

Bernier, O.J.[Olivier J.], Collobert, M., Feraud, R., Lemaire, V., Viallet, J.E., Collobert, D.,
MULTRAK: a system for automatic multiperson localization and tracking in real-time,
ICIP98(I: 136-140).
IEEE DOI 9810
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Tracking People, Re-Identification Issues, Occlusions .

Last update:Jan 13, 2022 at 22:02:22