16.7.4.4.2 Tracking Several People, Occlusions

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Unsupervised Tracklet Person Re-Identification,
PAMI(42), No. 7, July 2020, pp. 1770-1782.
IEEE DOI 2006
Cameras, Data models, Deep learning, Labeling, Adaptation models, Unsupervised learning, Training data, Person re-identification, multi-task deep learning BibRef

Wu, L.[Lin], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification,
PR(65), No. 1, 2017, pp. 238-250.
Elsevier DOI 1702
Linear discriminant analysis BibRef

Zhang, L.[Le], Shi, Z.L.[Zeng-Lin], Zhou, J.T.Y.[Joey Tian-Yi], Cheng, M.M.[Ming-Ming], Liu, Y.[Yun], Bian, J.W.[Jia-Wang], Zeng, Z.[Zeng], Shen, C.H.[Chun-Hua],
Ordered or Orderless: A Revisit for Video Based Person Re-Identification,
PAMI(43), No. 4, April 2021, pp. 1460-1466.
IEEE DOI 2103
Cameras, Feature extraction, Task analysis, Visualization, Video sequences, Aggregates, Bridges, Deep learning, video based person re-identification BibRef

Yan, C.[Cheng], Pang, G.S.[Guan-Song], Jiao, J.[Jile], Bai, X.[Xiao], Feng, X.T.[Xue-Tao], Shen, C.H.[Chun-Hua],
Occluded Person Re-Identification with Single-scale Global Representations,
ICCV21(11855-11864)
IEEE DOI 2203
Shape, Computational modeling, Pose estimation, Benchmark testing, Cameras, Data models, Image and video retrieval, Representation learning BibRef

Karanam, S.[Srikrishna], Li, Y.[Yang], Radke, R.J.[Richard J.],
Person re-identification with block sparse recovery,
IVC(60), No. 1, 2017, pp. 75-90.
Elsevier DOI 1704
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Particle dynamics and multi-channel feature dictionaries for robust visual tracking,
BMVC15(xx-yy).
DOI Link 1601
Person re-identification BibRef

Karanam, S.[Srikrishna], Gou, M.R.[Meng-Ran], Wu, Z.Y.[Zi-Yan], Rates-Borras, A.[Angels], Camps, O.I.[Octavia I.], Radke, R.J.[Richard J.],
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets,
PAMI(41), No. 3, March 2019, pp. 523-536.
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)
IEEE DOI 1812
Cameras, Feature extraction, Target tracking, Logic gates, Trajectory, Hidden Markov models BibRef

Madrigal, F.[Francisco], Hayet, J.B.[Jean-Bernard], Rivera, M.[Mariano],
Motion priors for multiple target visual tracking,
MVA(26), No. 2-3, April 2015, pp. 141-160.
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Earlier: A1, A3, A2:
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Springer DOI 1111

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Madrigal, F.[Francisco], Hayet, J.B.[Jean-Bernard],
Motion priors based on goals hierarchies in pedestrian tracking applications,
MVA(28), No. 3-4, May 2017, pp. 341-359.
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Evaluation of multiple motion models for multiple pedestrian visual tracking,
AVSS13(31-36)
IEEE DOI 1311
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AVSBS11(185-190).
IEEE DOI 1111
Bayes methods
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Jia, J.[Jieru], Ruan, Q.Q.[Qiu-Qi], An, G.[Gaoyun], Jin, Y.[Yi],
Multiple metric learning with query adaptive weights and multi-task re-weighting for person re-identification,
CVIU(160), No. 1, 2017, pp. 87-99.
Elsevier DOI 1706
Person re-identification BibRef

Zhao, C.R.[Cai-Rong], Wang, X.K.[Xue-Kuan], Wong, W.K.[Wai Keung], Zheng, W.S.[Wei-Shi], Yang, J.[Jian], Miao, D.Q.[Duo-Qian],
Multiple metric learning based on bar-shape descriptor for person re-identification,
PR(71), No. 1, 2017, pp. 218-234.
Elsevier DOI 1707
Person, re-identification BibRef

Yin, J.H.[Jia-Hang], Wu, A.[Ancong], Zheng, W.S.[Wei-Shi],
Fine-Grained Person Re-identification,
IJCV(128), No. 6, June 2020, pp. 1654-1672.
Springer DOI 2006
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Yang, Q.[Qize], Wu, A.[Ancong], Zheng, W.S.[Wei-Shi],
Person Re-Identification by Contour Sketch Under Moderate Clothing Change,
PAMI(43), No. 6, June 2021, pp. 2029-2046.
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.,
Fast Open-World Person Re-Identification,
IP(27), No. 5, May 2018, pp. 2286-2300.
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],
Saliency-Based Person Re-identification by Probability Histogram,
HIS16(III: 315-329).
Springer DOI 1704
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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,
PR(73), No. 1, 2018, pp. 275-288.
Elsevier DOI 1709
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Elsevier DOI 2109
Deep feature embedding BibRef

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,
CVIU(167), 2018, pp. 63-73.
Elsevier DOI 1804
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],
What-and-where to match: Deep spatially multiplicative integration networks for person re-identification,
PR(76), No. 1, 2018, pp. 727-738.
Elsevier DOI 1801
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And: Expression of concern: PR(121), 2022, pp. 108134.
Elsevier DOI 2109
Multiplicative integration gating BibRef

Li, S.Q.[Shuang-Qun], Ma, H.D.[Hua-Dong],
A Siamese inception architecture network for person re-identification,
MVA(28), No. 7, October 2017, pp. 725-736.
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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,
PR(75), No. 1, 2018, pp. 90-98.
Elsevier DOI 1712
Person re-identification BibRef

Ren, Y.T.[Yu-Tao], Li, X.L.[Xue-Long], Lu, X.Q.[Xiao-Qiang],
Feedback mechanism based iterative metric learning for person re-identification,
PR(75), No. 1, 2018, pp. 99-111.
Elsevier DOI 1712
Person re-identification BibRef

Sun, B.Y.[Bang-Yong], Ren, Y.T.[Yu-Tao], Lu, X.Q.[Xiao-Qiang],
Semisupervised Consistent Projection Metric Learning for Person Reidentification,
Cyber(52), No. 2, February 2022, pp. 738-747.
IEEE DOI 2202
Measurement, Training, Estimation, Image color analysis, Training data, Feature extraction, Data models, Discriminative, person reidentification BibRef

Jiang, Z.Q.[Zheng-Qiang], Huynh, D.Q.[Du Q.],
Multiple Pedestrian Tracking From Monocular Videos in an Interacting Multiple Model Framework,
IP(27), No. 3, March 2018, pp. 1361-1375.
IEEE DOI 1801
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,
DICTA10(328-334).
IEEE DOI 1012
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Jiang, Z.Q.[Zheng-Qiang], Huynh, D.Q.[Du Q.], Moran, W.[William], Challa, S.[Subhash],
Tracking pedestrians using smoothed colour histograms in an interacting multiple model framework,
ICIP11(2313-2316).
IEEE DOI 1201
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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,
MultMed(20), No. 3, March 2018, pp. 593-604.
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,
IET-Bio(7), No. 2, March 2018, pp. 125-135.
DOI Link 1802
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Barbosa, I.B.[Igor Barros], Cristani, M.[Marco], Caputo, B.[Barbara], Rognhaugen, A.[Aleksander], Theoharis, T.[Theoharis],
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.H.[Ai-Hong], Gao, B.[Bin], Luo, Z.K.[Zhang-Kai],
Improving Person Re-Identification by Efficient Pairwise-Specific CRC Coding in the XQDA Subspace,
IEICE(E101-D), No. 4, April 2018, pp. 1209-1212.
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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.Q.[Guo-Qi],
Person re-identification based on re-ranking with expanded k-reciprocal nearest neighbors,
JVCIR(58), 2019, pp. 486-494.
Elsevier DOI 1901
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],
Person re-identification with expanded neighborhoods distance re-ranking,
IVC(95), 2020, pp. 103875.
Elsevier DOI 2004
Person re-identification, Re-ranking, Expanded neighborhoods distance, Two-level neighborhoods BibRef

Li, Z.Y.[Zhi-Yong], Lv, J.Y.[Jing-Yi], Chen, Y.[Ying], Yuan, J.[Jin],
Person Re-Identification with Part Prediction Alignment,
CVIU(205), 2021, pp. 103172.
Elsevier DOI 2103
Person re-identification, Part-level feature, Prediction alignment, Global-local feature BibRef

Lisanti, G.[Giuseppe], Martinel, N.[Niki], Micheloni, C.[Christian], del Bimbo, A.[Alberto], Foresti, G.L.[Gian Luca],
From person to group re-identification via unsupervised transfer of sparse features,
IVC(83-84), 2019, pp. 29-38.
Elsevier DOI 1904
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Earlier: A1, A2, A4, A5, Only:
Group Re-identification via Unsupervised Transfer of Sparse Features Encoding,
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,
SPLetters(26), No. 6, June 2019, pp. 933-937.
IEEE DOI 1906
Feature extraction, Training, Reliability, Signal processing algorithms, Unsupervised learning, penalty term BibRef

Lei, J., Niu, L., Fu, H., Peng, B., Huang, Q., Hou, C.,
Person Re-Identification by Semantic Region Representation and Topology Constraint,
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IEEE DOI 1908
Measurement, Feature extraction, Semantics, Image color analysis, Reliability, Probes, Topology, Person re-identification, topological relationship BibRef

Wang, Z., Jiang, J., Yu, Y., Satoh, S.,
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IEEE DOI 1909
History, Cameras, Task analysis, Optimization, Trajectory, Video surveillance, Image retrieval, Person re-identification, log, cross-ranking BibRef

Kansal, K., Subramanyam, A.V., Wang, Z., Satoh, S.,
SDL: Spectrum-Disentangled Representation Learning for Visible-Infrared Person Re-Identification,
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IEEE DOI 2010
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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IEEE DOI 2011
Lighting, Cameras, Task analysis, Training, Feature extraction, Testing, Generators, Person re-identification, feature disentanglement BibRef

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Real-Time Person Re-identification at the Edge: A Mixed Precision Approach,
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Springer DOI 1909
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Jiang, L., Liang, C., Xu, D., Huang, W.,
Multi-Similarity Re-Ranking for Person Re-Identification,
ICIP19(1212-1216)
IEEE DOI 1910
contextual similarity, graph-based similarity, re-ranking, diffusion, person re-identification BibRef

Lu, Y., Hong, Z., Liu, B., Li, W., Yu, N.,
Dhff: Robust Multi-Scale Person Search by Dynamic Hierarchical Feature Fusion,
ICIP19(3935-3939)
IEEE DOI 1910
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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IEICE(E102-D), No. 11, November 2019, pp. 2230-2237.
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Wang, J.B.[Jia-Bao], Li, Y.[Yang], Jiao, S.S.[Shan-Shan], Miao, Z.[Zhuang], Zhang, R.[Rui],
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SP:IC(80), 2020, pp. 115674.
Elsevier DOI 1912
Person re-identification, Feature representation, Multi-level feature, Part-based feature, Grafting BibRef

Kim, K.[Kikyung], Byeon, M.[Moonsub], Choi, J.Y.[Jin Young],
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PRL(128), 2019, pp. 326-332.
Elsevier DOI 1912
Person re-identification, Re-ranking, Similarity metric BibRef

Cheng, D.[De], Li, Z.H.[Zhi-Hui], Gong, Y.H.[Yi-Hong], Zhang, D.W.[Ding-Wen],
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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

Liu, Z.[Zheng], Wang, Y.H.[Yun-Hong], Li, A.[Annan],
Hierarchical Integration of Rich Features for Video-Based Person Re-Identification,
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IEEE DOI 1912
Feature extraction, Legged locomotion, Semantics, Optical computing, Optical imaging, Visualization, multi-model ensemble BibRef

Cao, J.L.[Jia-Le], Pang, Y.W.[Yan-Wei], Han, J.G.[Jun-Gong], Gao, B.L.[Bo-Lin], Li, X.L.[Xue-Long],
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IP(29), 2020, pp. 3143-3152.
IEEE DOI 2002
Small-scale pedestrians, occluded pedestrians, location bootstrap, semantic transition BibRef

Wang, F.Y.[Feng-Yuan], Zhang, C.[Chi], Chen, S.H.[Sheng-Hui], Ying, G.[Guode], Lv, J.H.[Jian-Hua],
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Elsevier DOI 2002
Deep feature, Hand-crafted, Multimodal feature fusion, Person re-identification BibRef

Zhang, S.[Shun], Huang, J.B.[Jia-Bin], Lim, J.W.[Jong-Woo], Gong, Y.H.[Yi-Hong], Wang, J.[Jinjun], Ahuja, N.[Narendra], Yang, M.H.[Ming-Hsuan],
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IEEE DOI 2003
Feature extraction, Training, Optimization, Task analysis, Computational modeling, Data mining, Semantics, image retrieval. BibRef

Wan, C.Q.[Chao-Qun], Wu, Y.[Yue], Tian, X.M.[Xin-Mei], Huang, J.Q.[Jian-Qiang], Hua, X.S.[Xian-Sheng],
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IEEE DOI 2005
Feature extraction, Visualization, Cameras, Convolutional neural networks, Head, Torso, Legged locomotion, fine-grained representation BibRef

He, T.Y.[Tian-Yu], Shen, X.[Xu], Huang, J.Q.[Jian-Qiang], Chen, Z.B.[Zhi-Bo], Hua, X.S.[Xian-Sheng],
Partial Person Re-identification with Part-Part Correspondence Learning,
CVPR21(9101-9111)
IEEE DOI 2111
Measurement, Degradation, Deep learning, Image recognition, Benchmark testing, Pattern recognition BibRef

Lyu, C.J.[Cheng-Jin], Heyer-Wollenberg, P.[Patrick], Platisa, L.[Ljiljana], Goossens, B.[Bart], Veelaert, P.[Peter], Philips, W.[Wilfried],
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Person search, Re-identification, Pedestrian detection, Segmentation masks, Background clutters BibRef

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Wang, K.[Kan], Wang, P.F.[Peng-Fei], Ding, C.X.[Chang-Xing], Tao, D.C.[Da-Cheng],
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IEEE DOI 2103
Feature extraction, Training, Coherence, Visualization, Computational efficiency, Tools, Semantics, channel attention BibRef

Jin, X.[Xin], Lan, C.L.[Cui-Ling], Zeng, W.J.[Wen-Jun], Chen, Z.B.[Zhi-Bo],
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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)
IEEE DOI 2008
Semantics, Convolutional codes, Feature extraction, Benchmark testing, Aggregates, Stacking, Task analysis BibRef

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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Attention, Feature extraction, Cross correlation, Person Re-Identification, Surveillance BibRef

Fang, P.F.[Peng-Fei], Ji, P.[Pan], Petersson, L.[Lars], Harandi, M.[Mehrtash],
Set Augmented Triplet Loss for Video Person Re-Identification,
WACV21(464-473)
IEEE DOI 2106
Measurement, Prototypes, Benchmark testing, Task analysis, Standards BibRef

Xu, F.R.[Fu-Rong], Ma, B.P.[Bing-Peng], Chang, H.[Hong], Shan, S.G.[Shi-Guang],
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IEEE DOI 2009
Feature extraction, Training, Measurement, Robustness, Machine learning, Probes, Task analysis, Person re-identification, quadruplet BibRef

Hou, R.B.[Rui-Bing], Chang, H.[Hong], Ma, B.P.[Bing-Peng], Huang, R.[Rui], Shan, S.G.[Shi-Guang],
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CVPR21(2014-2023)
IEEE DOI 2111
Visualization, Codes, Computational modeling, Benchmark testing, Pattern recognition, Computational efficiency BibRef

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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Deng, X.S.[Xue-Song], Ma, B.P.[Bing-Peng], Chang, H.[Hong], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
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Xu, X., Ma, B.P.[Bing-Peng], Chang, H.[Hong], Chen, X.L.[Xi-Lin],
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ICIP17(1152-1156)
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Computer architecture, Recurrent neural networks, Target tracking, Training, Videos, Visualization, visual tracking BibRef

Song, W.[Wanru], Chen, C.H.[Chang-Hong], Zhao, Q.Q.[Qing-Qing], Liu, F.[Feng],
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Security application, Person re-identification, Pedestrian detection BibRef

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Person re-identification for smart cities: State-of-the-art and the path ahead,
PRL(138), 2020, pp. 282-289.
Elsevier DOI 1806
Person re-identification, Automated surveillance, People analysis, Smart city applications BibRef

Zhang, J.F.[Jian-Fu], Niu, L.[Li], Zhang, L.Q.[Li-Qing],
Person Re-Identification With Reinforced Attribute Attention Selection,
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Annotations, Task analysis, Noise measurement, Training, Image color analysis, Cameras, Feature extraction, sequential decision making BibRef

Yao, H.T.[Han-Tao], Xu, C.S.[Chang-Sheng],
Joint Person Objectness and Repulsion for Person Search,
IP(30), 2021, pp. 685-696.
IEEE DOI 2012
Probes, Search problems, Detectors, Proposals, Visualization, Noise measurement, Transforms, Detection-Matching person search, person re-identification BibRef

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IP(30), 2021, pp. 907-920.
IEEE DOI 2012
Correlation, Task analysis, Optimization, Training, Learning systems, Semantics, Lighting, Person re-identification, max pooling BibRef

Hu, X.Q.[Xiao-Qiang], Wei, D.[Dan], Wang, Z.Y.[Zi-Yang], Shen, J.L.[Jiang-Lin], Ren, H.J.[Hong-Juan],
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Pedestrian re-identification, Structural relationship, Action hypergraph, Saliency score BibRef

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Person Re-identification with Global-Local Background Bias Net,
JVCIR(74), 2021, pp. 102961.
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Person Re-identification, Body misalignment, Foreground features, Background information BibRef

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SMCS(51), No. 1, January 2021, pp. 354-369.
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,
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Springer DOI 2011
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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
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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

Behera, N.K.S.[Nayan Kumar Subhashis], Sa, P.K.[Pankaj Kumar], Bakshi, S.[Sambit], Padhy, R.P.[Ram Prasad],
Person re-identification: A taxonomic survey and the path ahead,
IVC(122), 2022, pp. 104432.
Elsevier DOI 2205
Survey, Re-Identification. Person re-identification, Visual surveillance, Computer vision BibRef

Cheng, D.[De], Zhou, J.Y.[Jing-Yu], Wang, N.N.[Nan-Nan], Gao, X.[Xinbo],
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id,
IP(31), 2022, pp. 3334-3346.
IEEE DOI 2205
Training, Task analysis, Feature extraction, Clustering algorithms, Cameras, Training data, Heuristic algorithms, Unsupervised, probability distillation BibRef

Pascotti-Valem, L.[Lucas], Guimarães-Pedronette, D.C.[Daniel Carlos],
Person Re-ID through unsupervised hypergraph rank selection and fusion,
IVC(123), 2022, pp. 104473.
Elsevier DOI 2206
Person Re-ID, Unsupervised, Hypergraph, Rank, Selection, Fusion BibRef

Yan, C.[Cheng], Pang, G.S.[Guan-Song], Wang, L.[Lei], Jiao, J.[Jile], Feng, X.T.[Xue-Tao], Shen, C.H.[Chun-Hua], Li, J.J.[Jing-Jing],
BV-Person: A Large-scale Dataset for Bird-view Person Re-identification,
ICCV21(10923-10932)
IEEE DOI 2203
Dataset, Re-Identification. Computational modeling, Benchmark testing, Cameras, Video surveillance, Search problems, Birds, Image and video retrieval BibRef

Yang, J.R.[Jin-Rui], Zhang, J.W.[Jia-Wei], Yu, F.[Fufu], Jiang, X.Y.[Xin-Yang], Zhang, M.D.[Meng-Dan], Sun, X.[Xing], Chen, Y.C.[Ying-Cong], Zheng, W.S.[Wei-Shi],
Learning to Know Where to See: A Visibility-Aware Approach for Occluded Person Re-identification,
ICCV21(11865-11874)
IEEE DOI 2203
Estimation error, Annotations, Computational modeling, Robustness, Noise measurement, Image and video retrieval, Recognition and classification 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
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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.Z.[Yu-Zhen], Liu, W.X.[Wen-Xi], Zhou, Y.C.[Yong-Cheng],
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
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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
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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
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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
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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
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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).
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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
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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
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Peralta, B.[Billy], Caro, L.[Luis], Soto, A.[Alvaro],
Unsupervised Local Regressive Attributes for Pedestrian Re-identification,
CIARP17(517-524).
Springer DOI 1802
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Earlier: A1, A3, Only:
Multi-target Tracking with Sparse Group Features and Position Using Discrete-Continuous Optimization,
HIS14(680-694).
Springer DOI 1504
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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
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Xu, B.[Bolei], Qiu, G.P.[Guo-Ping],
Unsupervised Person Re-identification via Graph-Structured Image Matching,
HIS16(III: 301-314).
Springer DOI 1704
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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
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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
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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
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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
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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
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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
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Earlier:
Efficient Multiple People Tracking Using Minimum Cost Arborescences,
GCPR14(265-276).
Springer DOI 1411
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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
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Online Multi-person Tracking by Tracker Hierarchy,
AVSS12(379-385).
IEEE DOI 1211
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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
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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
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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
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Heili, A.[Alexandre], Odobez, J.M.[Jean-Marc],
Parameter estimation and contextual adaptation for a multi-object tracking CRF model,
PETS13(14-21)
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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).
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Luo, X.H.[Xing-Han], Tan, R.T.[Robby T.], Veltkamp, R.C.[Remco C.],
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ICIP11(1877-1880).
IEEE DOI 1201
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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
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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
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Galoogahi, H.K.,
Tracking Groups of People in Presence of Occlusion,
PSIVT10(438-443).
IEEE DOI 1011
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Zen, G.[Gloria], Lanz, O.[Oswald], Messelodi, S.[Stefano], Ricci, E.[Elisa],
Tracking Multiple People with Illumination Maps,
ICPR10(3484-3487).
IEEE DOI 1008
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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
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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
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IEEE DOI 0806
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ICIAR07(581-592).
Springer DOI 0708
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Silhouette Based Human Motion Estimation,
DAGM04(294-301).
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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
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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,
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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,
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IEEE Top Reference. 0010
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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
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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
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Tracking People, Re-Identification Issues, Occlusions .

Last update:Jun 27, 2022 at 12:58:02