16.7.4.4.6 Convolutional Neural Network, CNN, Re-Identification Issues, Pedestrian Tracking

Chapter Contents (Back)
Motion, Human. Tracking. Convolutional Neural Network. CNN. Re-Identification.
See also Tracking People, Re-Identification Issues, Learning.
See also Adversarial Learning, GAN, Re-Identification Issues, Pedestrian Tracking.
See also Domain Adaption, Cross-Doimain, Learning, Re-Identification Issues.

Yang, F.[Fan], Lu, H.C.[Hu-Chuan], Yang, M.H.[Ming-Hsuan],
Robust Superpixel Tracking,
IP(23), No. 4, April 2014, pp. 1639-1651.
IEEE DOI 1404
computer vision BibRef

Yang, F.[Fan], Lu, H.C.[Hu-Chuan], Chen, Y.W.[Yen-Wei],
Human Tracking by Multiple Kernel Boosting with Locality Affinity Constraints,
ACCV10(IV: 39-50).
Springer DOI 1011
BibRef

Lu, H.C.[Hu-Chuan], Lu, S.P.[Shi-Peng], Chen, Y.W.[Yen-Wei],
Robust Tracking Based on Pixel-Wise Spatial Pyramid and Biased Fusion,
ACCV10(IV: 165-176).
Springer DOI 1011
BibRef

Yang, F.[Fan], Lu, H.C.[Hu-Chuan], Yang, M.H.[Ming-Hsuan],
Robust Visual Tracking via Multiple Kernel Boosting With Affinity Constraints,
CirSysVideo(24), No. 2, February 2014, pp. 242-254.
IEEE DOI 1403
object tracking BibRef

Ding, S.Y.[Sheng-Yong], Lin, L.[Liang], Wang, G.R.[Guang-Run], Chao, H.Y.[Hong-Yang],
Deep feature learning with relative distance comparison for person re-identification,
PR(48), No. 10, 2015, pp. 2993-3003.
Elsevier DOI 1507
Person re-identification BibRef

Zhang, K.H.[Kai-Hua], Liu, Q.S.[Qing-Shan], Wu, Y.[Yi], Yang, M.H.[Ming-Hsuan],
Robust Visual Tracking via Convolutional Networks Without Training,
IP(25), No. 4, April 2016, pp. 1779-1792.
IEEE DOI 1604
Computer architecture BibRef

Dominguez-Sanchez, A., Cazorla, M.[Miguel], Orts-Escolano, S.[Sergio],
Pedestrian Movement Direction Recognition Using Convolutional Neural Networks,
ITS(18), No. 12, December 2017, pp. 3540-3548.
IEEE DOI 1712
Biological neural networks, Estimation, Histograms, Neurons, Training, Trajectory, Pedestrian detection, pedestrian intention recognition BibRef

Huang, H.H.[Hong-He], Xu, Y.[Yi], Huang, Y.J.[Yan-Jie], Yang, Q.[Qian], Zhou, Z.G.[Zhi-Guo],
Pedestrian tracking by learning deep features,
JVCIR(57), 2018, pp. 172-175.
Elsevier DOI 1812
Pedestrian tracking, Convolutional neural networks, Optical flow BibRef

Franco, A.[Alexandre], Oliveira, L.[Luciano],
Convolutional covariance features: Conception, integration and performance in person re-identification,
PR(61), No. 1, 2017, pp. 593-609.
Elsevier DOI 1705
BibRef
Earlier:
A coarse-to-fine deep learning for person re-identification,
WACV16(1-7)
IEEE DOI 1606
Person re-identification. Face BibRef

McLaughlin, N., del Rincon, J.M.[J. Martinez], Miller, P.C.,
Person Reidentification Using Deep Convnets With Multitask Learning,
CirSysVideo(27), No. 3, March 2017, pp. 525-539.
IEEE DOI 1703
BibRef
Earlier:
Data-augmentation for reducing dataset bias in person re-identification,
AVSS15(1-6)
IEEE DOI 1511
image processing BibRef

Wang, J.[Jin], Wang, Z.[Zheng], Gao, C.X.[Chang-Xin], Sang, N.[Nong], Huang, R.,
DeepList: Learning Deep Features With Adaptive Listwise Constraint for Person Reidentification,
CirSysVideo(27), No. 3, March 2017, pp. 513-524.
IEEE DOI 1703
Computer architecture BibRef

Chen, S.Z., Guo, C.C., Lai, J.H.,
Deep Ranking for Person Re-Identification via Joint Representation Learning,
IP(25), No. 5, May 2016, pp. 2353-2367.
IEEE DOI 1604
Algorithm design and analysis BibRef

Yang, F.[Fan], Yan, K.[Ke], Lu, S.J.[Shi-Jian], Jia, H.Z.[Hui-Zhu], Xie, X.D.[Xiao-Dong], Gao, W.[Wen],
Attention driven person re-identification,
PR(86), 2019, pp. 143-155.
Elsevier DOI 1811
Person re-identification, Visual attention, Pose estimation, Deep neural networks BibRef

Zhou, S.P.[San-Ping], Wang, J.J.[Jin-Jun], Meng, D.Y.[De-Yu], Xin, X.M.[Xiao-Meng], Li, Y.B.[Yu-Bing], Gong, Y.H.[Yi-Hong], Zheng, N.N.[Nan-Ning],
Deep self-paced learning for person re-identification,
PR(76), No. 1, 2018, pp. 739-751.
Elsevier DOI 1801
Person re-identification BibRef

Lin, J., Ren, L., Lu, J.W.[Ji-Wen], Feng, J.J.[Jian-Jiang], Zhou, J.[Jie],
Consistent-Aware Deep Learning for Person Re-identification in a Camera Network,
CVPR17(3396-3405)
IEEE DOI 1711
Cameras, Image color analysis, Indexes, Lighting, Machine learning, Measurement, Optimal, matching BibRef

Cheng, D.[De], Gong, Y.H.[Yi-Hong], Chang, X.J.[Xiao-Jun], Shi, W.W.[Wei-Wei], Hauptmann, A.G.[Alexander G.], Zheng, N.N.[Nan-Ning],
Deep feature learning via structured graph Laplacian embedding for person re-identification,
PR(82), 2018, pp. 94-104.
Elsevier DOI 1806
Person re-identification, Structured, Graph Laplacian, Deep learning BibRef

Hu, L.[Liang], Hong, C.Q.[Chao-Qun], Zeng, Z.Q.A.[Zhi-Qi-Ang], Wang, X.D.[Xiao-Dong],
Two-stream person re-identification with multi-task deep neural networks,
RealTimeIP(14), No. 1, January 2018, pp. 947-954.
WWW Link. 1809
BibRef

Liu, Y.G.[Yong-Ge], Song, N.[Nan], Han, Y.H.[Ya-Hong],
Multi-cue fusion: Discriminative enhancing for person re-identification,
JVCIR(58), 2019, pp. 46-52.
Elsevier DOI 1901
Deep learning, Fusion strategy, Re-identification BibRef

Yu, P.J.[Pei-Jia], Zhao, Y.[Yong], Zhang, J.[Jing], Xie, X.Y.[Xiao-Yao],
Pedestrian detection using multi-channel visual feature fusion by learning deep quality model,
JVCIR(63), 2019, pp. 102579.
Elsevier DOI 1909
Convolutional neural networks, Pedestrian detection, VGG-16 net, RA block, Faster R-CNN, Quality model BibRef

Zheng, Z., Zheng, L., Yang, Y.,
Pedestrian Alignment Network for Large-scale Person Re-Identification,
CirSysVideo(29), No. 10, October 2019, pp. 3037-3045.
IEEE DOI 1910
convolutional neural nets, feature extraction, image matching, image retrieval, learning (artificial intelligence), pedestrians, deep learning BibRef

Shen, C., Qi, G., Jiang, R., Jin, Z., Yong, H., Chen, Y., Hua, X.,
Sharp Attention Network via Adaptive Sampling for Person Re-Identification,
CirSysVideo(29), No. 10, October 2019, pp. 3016-3027.
IEEE DOI 1910
convolutional neural nets, feature extraction, image sampling, learning (artificial intelligence), sharp attention network, CNN BibRef

Zhang, W.[Wei], Yu, X.D.[Xiao-Dong], He, X.Y.[Xuan-Yu],
Learning Bidirectional Temporal Cues for Video-Based Person Re-Identification,
CirSysVideo(28), No. 10, October 2018, pp. 2768-2776.
IEEE DOI 1811
Feature extraction, Measurement, Optical filters, Cameras, Video sequences, Recurrent neural networks, surveillance BibRef

Zhang, W.[Wei], Hu, S.N.[Sheng-Nan], Liu, K.[Kan], Zha, Z.J.[Zheng-Jun],
Learning Compact Appearance Representation for Video-Based Person Re-Identification,
CirSysVideo(29), No. 8, August 2019, pp. 2442-2452.
IEEE DOI 1908
Legged locomotion, Feature extraction, Task analysis, Cameras, Video sequences, Training, Data mining, CNN BibRef

Chen, Y.Q.[Yi-Qiang], Duffner, S.[Stefan], Stoian, A.[Andrei], Dufour, J.Y.[Jean-Yves], Baskurt, A.[Atilla],
Deep and low-level feature based attribute learning for person re-identification,
IVC(79), 2018, pp. 25-34.
Elsevier DOI 1811
Person re-identification, Soft-biometrics, Pedestrian attributes, Convolutional neural network BibRef

Wang, S.[Shengke], Zhang, X.Y.[Xiao-Yan], Chen, L.[Long], Zhou, H.Y.[Hui-Yu], Dong, J.Y.[Jun-Yu],
Asymmetric filtering-based dense convolutional neural network for person re-identification combined with Joint Bayesian and re-ranking,
JVCIR(57), 2018, pp. 262-271.
Elsevier DOI 1812
Person re-identification, Joint Bayesian, Deep convolutional neural networks, Multimodal features BibRef

Wang, S.[Shengke], Duan, L.H.[Liang-Hua], Yang, N.[Na], Dong, J.Y.[Jun-Yu],
Person re-identification with deep dense feature representation and Joint Bayesian,
ICIP17(3560-3564)
IEEE DOI 1803
Bayes methods, Cameras, Convolution, Feature extraction, Machine learning, Measurement, Training, verification BibRef

Yao, H.T.[Han-Tao], Zhang, S.L.[Shi-Liang], Hong, R.C.[Ri-Chang], Zhang, Y.D.[Yong-Dong], Xu, C.S.[Chang-Sheng], Tian, Q.[Qi],
Deep Representation Learning With Part Loss for Person Re-Identification,
IP(28), No. 6, June 2019, pp. 2860-2871.
IEEE DOI 1905
image classification, image representation, learning (artificial intelligence), neural nets, convolutional neural networks BibRef

Wu, Y.[Yu], Lin, Y.T.[Yu-Tian], Dong, X.Y.[Xuan-Yi], Yan, Y.[Yan], Bian, W.[Wei], Yang, Y.[Yi],
Progressive Learning for Person Re-Identification With One Example,
IP(28), No. 6, June 2019, pp. 2872-2881.
IEEE DOI 1905
convolutional neural nets, image sampling, learning (artificial intelligence), index-labeled data, few-example learning BibRef

Wu, L., Wang, Y., Gao, J., Li, X.,
Where-and-When to Look: Deep Siamese Attention Networks for Video-Based Person Re-Identification,
MultMed(21), No. 6, June 2019, pp. 1412-1424.
IEEE DOI 1906
Feature extraction, Measurement, Visualization, Spatiotemporal phenomena, Convolution, Computer architecture, visual attention BibRef

Ouyang, D.Q.[De-Qiang], Zhang, Y.H.[Yong-Hui], Shao, J.[Jie],
Video-based person re-identification via spatio-temporal attentional and two-stream fusion convolutional networks,
PRL(117), 2019, pp. 153-160.
Elsevier DOI 1901
Video-based person re-identification, Two-stream fusion, Spatio-temporal attention network, Heterogenous feature fusion BibRef

Li, D.G.[Dian-Gang], Gong, Y.H.[Yi-Hong], Cheng, D.[De], Shi, W.W.[Wei-Wei], Tao, X.Y.[Xiao-Yu], Chang, X.Y.[Xin-Yuan],
Consistency-Preserving deep hashing for fast person re-identification,
PR(94), 2019, pp. 207-217.
Elsevier DOI 1906
Convolutional neural network, Fast person re-identification, Deep hashing, Consistency preservation BibRef

Zheng, L., Huang, Y., Lu, H., Yang, Y.,
Pose-Invariant Embedding for Deep Person Re-Identification,
IP(28), No. 9, Sep. 2019, pp. 4500-4509.
IEEE DOI 1908
affine transforms, convolutional neural nets, face recognition, feature extraction, image matching, image representation, person re-identification BibRef

Zhou, S.P.[San-Ping], Wang, J.J.[Jin-Jun], Meng, D.Y.[De-Yu], Liang, Y.D.[Yu-Dong], Gong, Y.H.[Yi-Hong], Zheng, N.N.[Nan-Ning],
Discriminative Feature Learning With Foreground Attention for Person Re-Identification,
IP(28), No. 9, Sep. 2019, pp. 4671-4684.
IEEE DOI 1908
convolutional neural nets, image representation, object detection, regression analysis, supervised learning, foreground attentive feature learning BibRef

Quispe, R.[Rodolfo], Pedrini, H.[Helio],
Improved person re-identification based on saliency and semantic parsing with deep neural network models,
IVC(92), 2019, pp. 103809.
Elsevier DOI 1912
Person re-identification, Deep learning, Multi-clue guided learning, Human semantic parsing, Convolutional neural networks BibRef

Fan, X.[Xing], Jiang, W.[Wei], Luo, H.[Hao], Fei, M.J.[Meng-Juan],
SphereReID: Deep hypersphere manifold embedding for person re-identification,
JVCIR(60), 2019, pp. 51-58.
Elsevier DOI 1903
Person re-identification, Classification, Feature embedding, CNN, Hypersphere BibRef

Luo, H.[Hao], Jiang, W.[Wei], Zhang, X.[Xuan], Fan, X.[Xing], Qian, J.J.[Jing-Jing], Zhang, C.[Chi],
AlignedReID++: Dynamically matching local information for person re-identification,
PR(94), 2019, pp. 53-61.
Elsevier DOI 1906
Person re-identification, CNNs, Dynamically alignment BibRef

Zhang, W., Li, Y., Lu, W., Xu, X., Liu, Z., Ji, X.,
Learning Intra-Video Difference for Person Re-Identification,
CirSysVideo(29), No. 10, October 2019, pp. 3028-3036.
IEEE DOI 1910
feature extraction, image matching, image sequences, iterative methods, learning (artificial intelligence), CNN BibRef

Song, W., Li, S., Chang, T., Hao, A., Zhao, Q., Qin, H.,
Context-Interactive CNN for Person Re-Identification,
IP(29), 2020, pp. 2860-2874.
IEEE DOI 2001
Task analysis, Reinforcement learning, Feature extraction, Videos, Cameras, Context modeling, Clutter, Person re-identification, context-critic network BibRef

Serbetci, A.[Ayse], Akgul, Y.S.[Yusuf Sinan],
End-to-end training of CNN ensembles for person re-identification,
PR(104), 2020, pp. 107319.
Elsevier DOI 2005
Deep networks, Ensemble learning, Person re-identification BibRef

Li, J.N.[Jia-Ning], Zhang, S.L.[Shi-Liang], Huang, T.J.[Tie-Jun],
Multi-Scale Temporal Cues Learning for Video Person Re-Identification,
IP(29), 2020, pp. 4461-4473.
IEEE DOI 2003
Video Person ReID, Convolutional Neural Networks, Spatial Temporal Feature Learning BibRef

Zhu, X.G.[Xiao-Guang], Qian, J.C.[Jiu-Chao], Wang, H.Y.[Hao-Yu], Liu, P.L.[Pei-Lin],
Curriculum Enhanced Supervised Attention Network for Person Re-Identification,
SPLetters(27), 2020, pp. 1665-1669.
IEEE DOI 1806
Training, Feature extraction, Convolution, Detectors, Correlation, Machine learning, Complexity theory, Person re-identification supervised attention curriculum learning BibRef

Luo, H., Jiang, W., Fan, X., Zhang, C.,
STNReID: Deep Convolutional Networks With Pairwise Spatial Transformer Networks for Partial Person Re-Identification,
MultMed(22), No. 11, November 2020, pp. 2905-2913.
IEEE DOI 2010
Feature extraction, Task analysis, Training, Deep learning, Computational modeling, Fans, Image reconstruction, deep learning BibRef

Huang, Y.[Yewen], Lian, S.C.[Si-Cheng], Zhang, S.[Suian], Hu, H.F.[Hai-Feng], Chen, D.[Dihu], Su, T.[Tao],
Three-Dimension Transmissible Attention Network for Person Re-Identification,
CirSysVideo(30), No. 12, December 2020, pp. 4540-4553.
IEEE DOI 2012
Feature extraction, Convolution, Data mining, Visualization, Image recognition, feature representation BibRef

Zhang, R.M.[Rui-Mao], Lin, L.[Liang], Zhang, R.[Rui], Zuo, W.M.[Wang-Meng], Zhang, L.[Lei],
Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification,
IP(24), No. 12, December 2015, pp. 4766-4779.
IEEE DOI 1512
cryptography BibRef

Zhang, D.Y.[Dong-Yu], Wu, W.X.[Wen-Xi], Cheng, H.[Hui], Zhang, R.M.[Rui-Mao], Dong, Z.J.[Zhen-Jiang], Cai, Z.Q.[Zhao-Quan],
Image-to-Video Person Re-Identification With Temporally Memorized Similarity Learning,
CirSysVideo(28), No. 10, October 2018, pp. 2622-2632.
IEEE DOI 1811
Feature extraction, Measurement, Video sequences, Image color analysis, Histograms, Probes, Cameras, Person re-id, LSTM BibRef

Zhao, C.R.[Cai-Rong], Wang, X.K.[Xue-Kuan], Zuo, W.M.[Wang-Meng], Shen, F.[Fumin], Shao, L.[Ling], Miao, D.Q.[Duo-Qian],
Similarity learning with joint transfer constraints for person re-identification,
PR(97), 2020, pp. 107014.
Elsevier DOI 1910
Person re-identification, Feature extraction, Similarity learning BibRef

Zhao, C.R.[Cai-Rong], Lv, X.B.[Xin-Bi], Zhang, Z.[Zhang], Zuo, W.M.[Wang-Meng], Wu, J.[Jun], Miao, D.Q.[Duo-Qian],
Deep Fusion Feature Representation Learning With Hard Mining Center-Triplet Loss for Person Re-Identification,
MultMed(22), No. 12, December 2020, pp. 3180-3195.
IEEE DOI 2011
Feature extraction, Measurement, Training, Optimization, Computational modeling, Lighting, Task analysis, hard mining center-triplet loss BibRef

Zhao, C.R.[Cai-Rong], Chen, K.[Kang], Wei, Z.H.[Zhi-Hua], Chen, Y.P.[Yi-Peng], Miao, D.Q.[Duo-Qian], Wang, W.[Wei],
Multilevel triplet deep learning model for person re-identification,
PRL(117), 2019, pp. 161-168.
Elsevier DOI 1901
Multilevel feature extraction, Triplet architecture, Person re-identification BibRef

Meng, J.[Jingke], Wu, A.[Ancong], Zheng, W.S.[Wei-Shi],
Deep asymmetric video-based person re-identification,
PR(93), 2019, pp. 430-441.
Elsevier DOI 1906
Person re-identification, Visual surveillance BibRef

Bai, X.[Xiang], Yang, M.K.[Ming-Kun], Huang, T.T.[Teng-Teng], Dou, Z.Y.[Zhi-Yong], Yu, R.[Rui], Xu, Y.C.[Yong-Chao],
Deep-Person: Learning discriminative deep features for person Re-Identification,
PR(98), 2020, pp. 107036.
Elsevier DOI 1911
Person Re-ID, LSTM, Triplet loss, End-to-end BibRef

Wang, Z., Jiang, J., Wu, Y., Ye, M., Bai, X., Satoh, S.,
Learning Sparse and Identity-Preserved Hidden Attributes for Person Re-Identification,
IP(29), 2020, pp. 2013-2025.
IEEE DOI 2001
Semantics, Deep learning, Visualization, Feature extraction, Image reconstruction, Clothing, Training, Person re-identification, discrimination BibRef

Li, H.F.[Hua-Feng], Xu, J.J.[Jia-Jia], Yu, Z.T.[Zheng-Tao], Luo, J.B.[Jie-Bo],
Jointly Learning Commonality and Specificity Dictionaries for Person Re-Identification,
IP(29), 2020, pp. 7345-7358.
IEEE DOI 2007
Dictionaries, Feature extraction, Cameras, Image coding, Deep learning, Visualization, Person re-identification, specificity dictionary BibRef

Tagore, N.K.[Nirbhay Kumar], Singh, A.[Ayushman], Manche, S.[Sumanth], Chattopadhyay, P.[Pratik],
Person re-identification from appearance cues and deep Siamese features,
JVCIR(75), 2021, pp. 103029.
Elsevier DOI 2103
Hierarchical re-identification approach, Color-based clustering, Silhouette part-based analysis, IIT(BHU) re-identification data set BibRef

Chen, X., Zheng, X., Lu, X.,
Bidirectional Interaction Network for Person Re-Identification,
IP(30), 2021, pp. 1935-1948.
IEEE DOI 2101
Feature extraction, Convolution, Pose estimation, Task analysis, Semantics, Cameras, Lighting, Person re-identification, bilinear pooling BibRef

Sun, Y.F.[Yi-Fan], Zheng, L.[Liang], Li, Y.L.[Ya-Li], Yang, Y.[Yi], Tian, Q.[Qi], Wang, S.J.[Sheng-Jin],
Learning Part-based Convolutional Features for Person Re-Identification,
PAMI(43), No. 3, March 2021, pp. 902-917.
IEEE DOI 2102
Pose estimation, Training, Feature extraction, Deep learning, Semantics, Sun, Labeling, Person re-identification, part refinement BibRef

Liu, C.[Cen], Guo, L.J.[Li-Jun], Zhang, R.[Rong],
HLFNet: High-low Frequency Network for Person Re-Identification,
SPLetters(28), 2021, pp. 1140-1144.
IEEE DOI 2106
Feature extraction, Training, Data mining, Convolution, Task analysis, Image color analysis, Cameras, local and global feature BibRef

Bai, Y.[Yan], Wang, C.[Ce], Lou, Y.H.[Yi-Hang], Liu, J.[Jun], Duan, L.Y.[Ling-Yu],
Hierarchical Connectivity-Centered Clustering for Unsupervised Domain Adaptation on Person Re-Identification,
IP(30), 2021, pp. 6715-6729.
IEEE DOI 2108
Feature extraction, Visualization, Training, Clustering methods, Data mining, Adaptation models, Training data, graph convolutional networks BibRef

Rossi, L.[Luca], Paolanti, M.[Marina], Pierdicca, R.[Roberto], Frontoni, E.[Emanuele],
Human trajectory prediction and generation using LSTM models and GANs,
PR(120), 2021, pp. 108136.
Elsevier DOI 2109
Trajectory generation, Trajectory prediction, LSTM, GANs BibRef


Matsukawa, T.[Tetsu], Suzuki, E.[Einoshin],
Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification,
ICPR21(8408-8415)
IEEE DOI 2105
Training, Legged locomotion, Cameras, Spatial databases, Pattern recognition, Security, Convolutional neural networks BibRef

Breckon, T.P.[Toby P.], Alsehaim, A.[Aishah],
Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-identification,
ICPR21(5190-5197)
IEEE DOI 2105
Training, Solid modeling, Convolution, Surveillance, Transfer learning, Streaming media BibRef

Li, M.K.[Ming-Kun], Li, C.G.[Chun-Guang], Guo, R.[Ruopei], Guo, J.[Jun],
Self-paced Bottom-up Clustering Network with Side Information for Person Re-Identification,
ICPR21(1430-1437)
IEEE DOI 2105
Training, Annealing, Convolution, Merging, Process control, Benchmark testing, Cameras BibRef

Zhang, L.[Lei], Jiang, N.[Na], Xu, Y.[Yue], Diao, Q.[Qishuai], Zhou, Z.[Zhong], Wu, W.[Wei],
Pose Variation Adaptation for Person Re-identification,
ICPR21(6996-7003)
IEEE DOI 2105
Training, Deep learning, Adaptation models, Image synthesis, Surveillance, Shape measurement, Feature extraction BibRef

Hong, P.X.[Pei-Xian], Wu, A.C.[An-Cong], Zheng, W.S.[Wei-Shi],
Semi-supervised Person Re-identification by Attribute Similarity Guidance,
ICPR21(6471-6477)
IEEE DOI 2105
Training, Deep learning, Annotations, Semantics, Semisupervised learning, Cameras, Pattern recognition BibRef

Chen, C.[Chen], Dou, H.[Hao], Hu, X.[Xiyuan], Peng, S.[Silong],
Deep Top-rank Counter Metric for Person Re-identification,
ICPR21(2732-2739)
IEEE DOI 2105
Measurement, Deep learning, Training, Learning systems, Boosting, Pattern recognition, Usability, person re-identification, deep learning BibRef

Wang, C.H.[Chang-Hao], Zhou, J.[Jun], Duan, X.F.[Xian-Fei], Zhang, G.[Guanwen], Zhou, W.[Wei],
Recurrent Deep Attention Network for Person Re-Identification,
ICPR21(4276-4281)
IEEE DOI 2105
Visualization, Reinforcement learning, Benchmark testing, Video surveillance, Cameras, Concrete, Cognition BibRef

Lin, C., Guo, R., Li, M., Qi, X., Li, C.G.,
Learning Convolution Feature Aggregation via Edge Attention Convolution Network for Person Re-Identification,
VCIP20(539-542)
IEEE DOI 2102
Convolution, Image edge detection, Feature extraction, Measurement, Entropy, Smoothing methods, Training, Person ReID, Over-smoothing BibRef

Mohamed, A., Qian, K., Elhoseiny, M., Claudel, C.,
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction,
CVPR20(14412-14420)
IEEE DOI 2008
Trajectory, Predictive models, Convolution, Kernel, Convolutional neural networks, Autonomous vehicles, Data models BibRef

Subramaniam, A., Nambiar, A., Mittal, A.,
Co-Segmentation Inspired Attention Networks for Video-Based Person Re-Identification,
ICCV19(562-572)
IEEE DOI 2004
feature extraction, image segmentation, object detection, video signal processing, video surveillance, video-based person, Convolution BibRef

Liu, Z., Wang, J., Gong, S., Tao, D., Lu, H.,
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification,
ICCV19(6121-6130)
IEEE DOI 2004
convolutional neural nets, image annotation, learning (artificial intelligence), Computational modeling BibRef

Quan, R., Dong, X., Wu, Y., Zhu, L., Yang, Y.,
Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification,
ICCV19(3749-3758)
IEEE DOI 2004
convolutional neural nets, image classification, part-aware ConvNet, person re-identification, Search problems BibRef

Dai, Z., Chen, M., Gu, X., Zhu, S., Tan, P.,
Batch DropBlock Network for Person Re-Identification and Beyond,
ICCV19(3690-3700)
IEEE DOI 2004
convolutional neural nets, feature extraction, learning (artificial intelligence), Cameras BibRef

Zhou, K., Yang, Y., Cavallaro, A., Xiang, T.,
Omni-Scale Feature Learning for Person Re-Identification,
ICCV19(3701-3711)
IEEE DOI 2004
Code, Re-Identification.
WWW Link. convolutional neural nets, feature extraction, graph theory, image representation, learning (artificial intelligence), Convolution BibRef

Zhu, Y., Guo, X., Liu, J., Jiang, Z.,
Multi-Branch Context-Aware Network for Person Re-Identification,
ICIP19(2274-2278)
IEEE DOI 1910
Person re-identification, convolutional neural networks, contextual dependencies, attention module BibRef

Xin, X., Wu, X., Wang, Y., Wang, J.,
Deep Self-Paced Learning for Semi-Supervised Person Re-Identification Using Multi-View Self-Paced Clustering,
ICIP19(2631-2635)
IEEE DOI 1910
Person Re-Identification, Semi-Supervised Learning, Convolutional Neural Network, Multi-View Clustering, Self-paced Learning BibRef

Sun, X., Zhang, N., Chen, Q., Cao, Y., Liu, B.,
People Re-Identification by Multi-Branch CNN with Multi-Scale Features,
ICIP19(2269-2273)
IEEE DOI 1910
People re-identification, Multi-branch CNN, Multi-scale features, Triplet loss BibRef

Yin, J., Li, B., Wan, F., Zhu, Y.,
A New Data Selection Strategy for One-Shot Video-Based Person Re-Identification,
ICIP19(1227-1231)
IEEE DOI 1910
Person re-identification, one-shot, semi-supervised, deep learning BibRef

Ponce-López, V.[Víctor], Burghardt, T.[Tilo], Hannunna, S.[Sion], Damen, D.[Dima], Masullo, A.[Alessandro], Mirmehdi, M.[Majid],
Semantically Selective Augmentation for Deep Compact Person Re-Identification,
PersonContext18(II:551-561).
Springer DOI 1905
BibRef

Li, Y., Yang, F., Liu, Y., Yeh, Y., Du, X., Wang, Y.F.,
Adaptation and Re-identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-identification,
Crowd18(285-2856)
IEEE DOI 1812
Adaptation models, Training, Cameras, Task analysis, Data models, Visualization, Feature extraction BibRef

Chen, D., Xu, D., Li, H., Sebe, N., Wang, X.,
Group Consistent Similarity Learning via Deep CRF for Person Re-identification,
CVPR18(8649-8658)
IEEE DOI 1812
Training, Measurement, Probes, Neural networks, Estimation, Graphical models, Task analysis BibRef

Shen, Y., Xiao, T., Li, H., Yi, S., Wang, X.,
End-to-End Deep Kronecker-Product Matching for Person Re-identification,
CVPR18(6886-6895)
IEEE DOI 1812
Estimation, Neural networks, Measurement, Layout, Semantics BibRef

Chen, D.P.[Da-Peng], Li, H.S.[Hong-Sheng], Liu, X.[Xihui], Shen, Y.[Yantao], Shao, J.[Jing], Yuan, Z.J.[Ze-Jian], Wang, X.G.[Xiao-Gang],
Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association,
ECCV18(XVI: 56-73).
Springer DOI 1810
BibRef

Jiao, F., Bhanu, B.,
Deepagent: An Algorithm Integration Approach for Person Re-Identification,
ICIP18(853-857)
IEEE DOI 1809
Probes, Feature extraction, Training, Cameras, Learning (artificial intelligence), Neural networks, Testing, Algorithm integration BibRef

Chen, W., Chen, X., Zhang, J., Huang, K.,
Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification,
CVPR17(1320-1329)
IEEE DOI 1711
Feature extraction, Loss measurement, Machine learning, Probes, Testing, Training BibRef

Marchwica, P., Jamieson, M., Siva, P.,
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification,
CRV18(297-304)
IEEE DOI 1812
Cameras, Training, Buildings, Network architecture, Testing, Training data, Airports, Person Re-identification, Deep Learning BibRef

Zhao, Y., Luo, S., Yang, Y., Song, M.,
DeepSSH: Deep Semantic Structured Hashing for Explainable Person Re-Identification,
ICIP18(1653-1657)
IEEE DOI 1809
CNN, person re-identification, hashing BibRef

Yu, J., Ko, D., Moon, H., Jeon, M.,
Deep Discriminative Representation Learning for Face Verification and Person Re-Identification on Unconstrained Condition,
ICIP18(1658-1662)
IEEE DOI 1809
Task analysis, Face, Feature extraction, Measurement, Training, Optimization, Probes, person re-identification BibRef

Zhang, Y.H.[Yong-Hui], Shao, J.[Jie], Ouyang, D.Q.[De-Qiang], Shen, H.T.[Heng Tao],
Person Re-identification Using Two-Stage Convolutional Neural Network,
ICPR18(3341-3346)
IEEE DOI 1812
Feature extraction, Image sequences, Convolutional neural networks, Computer aided instruction, Training BibRef

Xiang, J., Lin, R., Hou, J., Huang, W.,
Person Re-identification Based on Feature Fusion and Triplet Loss Function,
ICPR18(3477-3482)
IEEE DOI 1812
Feature extraction, Semantics, Measurement, Task analysis, Neural networks, Convolution, Training BibRef

Du, P., Song, Y., Zhang, Y.,
Which Part is Better: Multi-Part Competition Network for person Re-Identification,
ICPR18(1634-1639)
IEEE DOI 1812
Feature extraction, Head, Detectors, Measurement, Convolution, Magnetic heads, Legged locomotion, person re-identification, Multi-Part Competition Network BibRef

Lejbølle, A.R., Krogh, B., Nasrollahi, K., Moeslund, T.B.,
Attention in Multimodal Neural Networks for Person Re-identification,
Crowd18(292-2928)
IEEE DOI 1812
Feature extraction, Convolution, Data mining, Fuses, Cameras, Image color analysis, Neural networks BibRef

Guo, Y., Cheung, N.,
Efficient and Deep Person Re-identification Using Multi-level Similarity,
CVPR18(2335-2344)
IEEE DOI 1812
Feature extraction, Convolution, Visualization, Task analysis, Measurement, Image color analysis, Computational modeling BibRef

Chen, Y.Q.[Yi-Qiang], Duffner, S.[Stefan], Stoian, A.[Andrei], Dufour, J.Y.[Jean-Yves], Baskurt, A.[Atilla],
Person Re-identification Using Group Context,
ACIVS18(392-401).
Springer DOI 1810
BibRef
And:
Person Re-Identification with a Body Orientation-Specific Convolutional Neural Network,
ACIVS18(26-37).
Springer DOI 1810
BibRef
And: A1, A2, A5, A3, A4:
Similarity Learning with Listwise Ranking for Person Re-Identification,
ICIP18(843-847)
IEEE DOI 1809
Training, Loss measurement, Probes, Robustness, Machine learning, Manganese, Video surveillance, Similarity learning BibRef

Liu, J., Yang, Z., Zhang, T., Xiong, H.,
Multi-part compact bilinear CNN for person re-identification,
ICIP17(2309-2313)
IEEE DOI 1803
Convolution, Convolutional neural networks, Feature extraction, Histograms, Machine learning, Measurement, Task analysis, multi-part BibRef

Kim, J., Yoo, C.D.,
Deep partial person re-identification via attention model,
ICIP17(3425-3429)
IEEE DOI 1803
Convolution, Feature extraction, Kernel, Measurement, Neural networks, Probes, Training, Attention model, RoI Pooling BibRef

Chung, D., Tahboub, K., Delp, E.J.,
A Two Stream Siamese Convolutional Neural Network for Person Re-identification,
ICCV17(1992-2000)
IEEE DOI 1802
convolution, image matching, neural nets, video surveillance, matching accuracy, person re-identification, Streaming media BibRef

Chen, Y., Duffner, S., Stoian, A., Dufour, J.Y., Baskurt, A.,
Triplet CNN and pedestrian attribute recognition for improved person re-identification,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, image representation, learning (artificial intelligence), multilayer perceptrons, Visualization BibRef

Rahimpour, A., Liu, L., Taalimi, A., Song, Y., Qi, H.,
Person re-identification using visual attention,
ICIP17(4242-4246)
IEEE DOI 1803
Computational modeling, Computer architecture, Measurement, Neural networks, Task analysis, Training, Visualization, Deep CNN, Triplet loss BibRef

Carr, S.B.P.,
Deep spatial pyramid for person re-identification,
AVSS17(1-6)
IEEE DOI 1806
cellular neural nets, feature extraction, image classification, image representation, image sensors, Training BibRef

Zhang, Y.H.[Yi-Hao], Wang, W.M.[Wen-Min], Wang, J.Z.[Jin-Zhuo],
Deep discriminative network with inception module for person re-identification,
VCIP17(1-4)
IEEE DOI 1804
cameras, computer vision, feature extraction, feedforward neural nets, image classification, image matching, person re-identification BibRef

Lin, S., Li, C.T.,
End-to-End Correspondence and Relationship Learning of Mid-Level Deep Features for Person Re-Identification,
DICTA17(1-6)
IEEE DOI 1804
feature extraction, image matching, image representation, learning (artificial intelligence), neural nets, Training BibRef

Zhao, L., Li, X., Zhuang, Y., Wang, J.,
Deeply-Learned Part-Aligned Representations for Person Re-identification,
ICCV17(3239-3248)
IEEE DOI 1802
image matching, image representation, learning (artificial intelligence), neural nets, Probes BibRef

Zhou, S., Wang, J., Wang, J., Gong, Y., Zheng, N.,
Point to Set Similarity Based Deep Feature Learning for Person Re-Identification,
CVPR17(5028-5037)
IEEE DOI 1711
Cameras, Feature extraction, Learning systems, Lighting, Machine learning, Measurement, Robustness BibRef

Li, D., Chen, X., Zhang, Z., Huang, K.,
Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification,
CVPR17(7398-7407)
IEEE DOI 1711
Clutter, Feature extraction, Image color analysis, Kernel, Machine learning, Visualization BibRef

Zhang, L.[Li], Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Learning a Deep Embedding Model for Zero-Shot Learning,
CVPR17(3010-3019)
IEEE DOI 1711
BibRef
Earlier:
Learning a Discriminative Null Space for Person Re-identification,
CVPR16(1239-1248)
IEEE DOI 1612
Computational modeling, Neural networks, Prototypes, Search problems, Semantics, Training, Visualization BibRef

Varior, R.R.[Rahul Rama], Haloi, M.[Mrinal], Wang, G.[Gang],
Gated Siamese Convolutional Neural Network Architecture for Human Re-identification,
ECCV16(VIII: 791-808).
Springer DOI 1611
BibRef

Wu, S., Chen, Y.C., Li, X., Wu, A.C., You, J.J., Zheng, W.S.,
An enhanced deep feature representation for person re-identification,
WACV16(1-8)
IEEE DOI 1606
Convolution BibRef

Ahmed, E.[Ejaz], Jones, M.[Michael], Marks, T.K.[Tim K.],
An improved deep learning architecture for person re-identification,
CVPR15(3908-3916)
IEEE DOI 1510
BibRef

Schumann, A., Stiefelhagen, R.[Rainer],
Person Re-identification by Deep Learning Attribute-Complementary Information,
Re-Id17(1435-1443)
IEEE DOI 1709
Cameras, Machine learning, Robustness, Semantics, Training, Training, data BibRef

Schumann, A., Bauml, M.[Martin], Stiefelhagen, R.[Rainer],
Person tracking-by-detection with efficient selection of part-detectors,
AVSS13(43-50)
IEEE DOI 1311
computer graphics BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Adversarial Learning, GAN, Re-Identification Issues, Pedestrian Tracking .


Last update:Nov 1, 2021 at 09:26:50