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A comprehensive survey on person re-identification approaches: various aspects

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Abstract

Person re-identification (Re-ID) is an application of video surveillance and has become popular among Computer Vision and Image processing research communities since last decade due to having its strong safety and security potential. It is the process of identifying a person of interest in distributed non-overlapping camera views. Person re-identification has broad application in maintaining the security by re-identifying the malicious persons in networking cameras. Now a days terrorist and criminal activities are increasing day by day and it is utmost important to re-identify a person of interest at public places like – shopping malls, railway stations, airports, huge public events etc. A lot of challenges are involved in the re-identification process like variation in lighting condition, different poses and viewpoints, blurring effect, image resolution, background changes etc. Basically 2 types of datasets (image based, video based) are designed for re-identification purpose based on application and approaches. This paper includes the study of many popular datasets like ViPER, iLIDS, Market1501, DukeMTMC4ReID, CUHK01, CHUK02, CHUK03, PRID2011 etc. including the various parameters (no of persons, no of images, no of cameras, size of frames etc.) and challenges involved in that. In this paper various aspects of person re-identification approaches are discussed including temporal, spatial, feature, distance metric, machine learning, automation etc. to get the comprehensive and exhaustive idea of person re-identification methods.

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References

  1. ACCL (2019) How Many CCTV Cameras Are There in London? https://network-data-cabling.co.uk/blog/how-many-cctv-security-cameras-in-london/. Accessed 13 March 2020

  2. Ahmed E, Jones M, Marks TK (2015) An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3908–3916

    Google Scholar 

  3. Albiol A, Oliver J, Mossi J (2012) Who is who at different cameras: people re-identification using depth cameras. IET Comput Vis 6(5):378–387

    Article  MathSciNet  Google Scholar 

  4. Annesley J, Orwell J, Renno J (2005) Evaluation of MPEG7 color descriptors for visual surveillance retrieval. In: 2005 IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, VS-PETS, 2005, pp 105–112

    Chapter  Google Scholar 

  5. Avraham, T., Gurvich, I., Lindenbaum, M., & Markovitch, S. (2012). Learning implicit transfer for person re-identification. In European conference on computer vision (pp. 381–390). Springer, Berlin, Heidelberg

  6. Bak S, Corvee E, Bremond F, Thonnat M (2010a) Person re-identification using haar-based and dcd-based signature. In: 2010 7th IEEE international conference on advanced video and signal based surveillance. IEEE, pp 1–8

    Google Scholar 

  7. Bak S, Corvee E, Bremond F, Thonnat M (2010b) Person re-identification using spatial covariance regions of human body parts. In: 7th IEEE international conference on advanced video and signal based surveillance. IEEE, pp 435–440

    Google Scholar 

  8. Bąk S, Corvee E, Brémond F, Thonnat M (2011) Multiple-shot human re-identification by mean Riemannian covariance grid. In: 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, pp 179–184

    Chapter  Google Scholar 

  9. Bak S, Zaidenberg S, Boulay B, Bremond F (2014) Improving person re-identification by viewpoint cues. In: Proceedings of 11th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE, pp 175–180

    Google Scholar 

  10. Bak S, Carr P, Lalonde JF (2018) Domain adaptation through synthesis for unsupervised person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 189–205

    Google Scholar 

  11. Balazia M, Sojka P (2017) You are how you walk: uncooperative MoCap gait identification for video surveillance with incomplete and noisy data. In: 2017 IEEE international joint conference on biometrics (IJCB). IEEE, pp 208–215

    Chapter  Google Scholar 

  12. Baltieri D, Vezzani R, Cucchiara R (2011) 3dpes: 3d people dataset for surveillance and forensics. In: Proceedings of the 2011 joint ACM workshop on human gesture and behavior understanding, pp 59–64

    Chapter  Google Scholar 

  13. Bazzani L, Cristani M, Perina A, Farenzena M, Murino V (2010) Multiple-shot person re-identification by HPE signature. In: 2010 20th international conference on pattern recognition. IEEE, pp 1413–1416

    Chapter  Google Scholar 

  14. Bazzani L, Cristani M, Murino V (2013) Symmetry-driven accumulation of local features for human characterization and re-identification. Comput Vis Image Underst 117(2):130–144

    Article  Google Scholar 

  15. Bedagkar-Gala A, Shah SK (2014a) A survey of approaches and trends in person re-identification. Image Vis Comput 32(4):270–286

    Article  Google Scholar 

  16. Bedagkar-Gala A, Shah SK (2014b) Gait-assisted person re-identification in wide area surveillance. In: Asian conference on computer vision. Springer, Cham, pp 633–649

    Google Scholar 

  17. Bhuiyan A, Mirmahboub B, Perina A, Murino V (2015) Person re-identification using robust brightness transfer functions based on multiple detections. In: International conference on image analysis and processing. Springer, Cham, pp 449–459

    Google Scholar 

  18. Bolle RM, Connell JH, Pankanti S, Ratha NK, Senior AW (2005) The relation between the ROC curve and the CMC. In: Fourth IEEE workshop on automatic identification advanced technologies (AutoID'05). IEEE, pp 15–20

    Chapter  Google Scholar 

  19. Bouchrika I, Carter JN, Nixon MS (2016) Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras. Multimed Tools Appl 75(2):1201–1221

    Article  Google Scholar 

  20. Cai Y, Pietikäinen M (2010) Person re-identification based on global color context. In: Asian conference on computer vision. Springer, Berlin, Heidelberg, pp 205–215

    Google Scholar 

  21. Calderara S, Cucchiara R, Prati A (2008) Bayesian-competitive consistent labeling for people surveillance. IEEE Trans Pattern Anal Mach Intell 30(2):354–360

    Article  Google Scholar 

  22. Chattopadhyay P, Sural S, Mukherjee J (2015) Information fusion from multiple cameras for gait-based re-identification and recognition. IET Image Process 9(11):969–976

    Article  Google Scholar 

  23. Chen J, Zhang Z, Wang Y (2014) Relevance metric learning for person re-identification by exploiting global similarities. In: 2014 22nd international conference on pattern recognition, pp 1657–1662

    Chapter  Google Scholar 

  24. Chen D, Yuan Z, Hua G, Zheng N, Wang J (2015a) Similarity learning on an explicit polynomial kernel feature map for person re-identification. In: Proceeding of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1565–1573

    Google Scholar 

  25. Chen J, Zhang Z, Wang Y (2015b) Relevance metric learning for person re-identification by exploiting listwise similarities. IEEE Trans Image Process 24(12):4741–4755

    Article  MathSciNet  MATH  Google Scholar 

  26. Chen YC, Zheng WS, Lai J (2015c) Mirror representation for modeling view-specific transform in person re-identification. In: Twenty-fourth international joint conference on artificial intelligence (IJCAI), pp 3402–3408

    Google Scholar 

  27. Chen Y, Zhu X, Gong S (2017) Person re-identification by deep learning multi-scale representations. In: Proceeding of the IEEE international conference on computer vision workshops (ICCVW), pp 2590–2600

    Google Scholar 

  28. Chen Y, Zhu X, Gong S (2018) Deep association learning for unsupervised video person re-identification. arXiv preprint arXiv:1808.07301

  29. Cheng DS, Cristani M, Stoppa M, Bazzani L, Murino V (2011) Custom pictorial structures for re-identification. In: Proceedings of the British machine vision conference (Bmvc) (Vol. 1, No. 2, p. 6)

    Google Scholar 

  30. Cho Y, Yoon K (2016) Improving person re-identification via pose-aware multi-shot matching. In: Proceeding of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1354–1362

    Google Scholar 

  31. Cong DNT, Khoudour L, Achard C, Meurie C, Lezoray O (2010) People re-identification by spectral classification of silhouettes. Signal Process 90(8):2362–2374

    Article  MATH  Google Scholar 

  32. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol 1. IEEE, pp 886–893

    Google Scholar 

  33. D'Angelo A, Dugelay JL (2011) People re-identification in camera networks based on probabilistic color histograms. In: Visual information processing and communication II (Vol. 7882, p. 78820K). International Society for Optics and Photonics

    Google Scholar 

  34. Das A, Chakraborty A, Roy-Chowdhury A (2014) Consistent re-identification in a camera network. In: European conference on computer vision. Springer, Cham, pp 330–345

    Google Scholar 

  35. De Oliveira IO, de Souza Pio JL (2009) People reidentification in a camera network. In: 2009 eighth IEEE international conference on dependable, autonomic and secure computing. IEEE, pp 461–466

    Chapter  Google Scholar 

  36. DeCann B, Ross A (2015) Modelling errors in a biometric re-identification system. IET Biometrics 4(4):209–219

    Article  Google Scholar 

  37. Deng W, Zheng L, Ye Q, Kang G, Yang Y, Jiao J (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 994–1003

    Google Scholar 

  38. Dikmen M, Akbas E, Huang T, Ahuja N (2011) Pedestrian recognition with a learned metric. In: Asian conference on computer vision (ACCV). Springer, Berlin Heidelberg, pp 501–512

    Google Scholar 

  39. Ding G, Khan S, Tang Z, Porikli F (2020) Feature mask network for person re-identification. Pattern Recogn Lett 137:91–98

    Article  Google Scholar 

  40. Doretto G, Sebastian T, Tu P, Rittscher J (2011) Appearance-based person reidentification in camera networks: problem overview and current approaches. J Ambient Intell Humaniz Comput 2(2):127–151

    Article  Google Scholar 

  41. Du Y, Ai H, Lao S (2012) Evaluation of color spaces for person re-identification. In: Proceedings of the international conference on pattern recognition. IEEE, pp 1371–1374

    Google Scholar 

  42. Ess A, Leibe B, Van Gool L (2007) Depth and appearance for mobile scene analysis. In: 2007 IEEE 11th international conference on computer vision. IEEE, pp 1–8

    Google Scholar 

  43. Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 2360–2367

    Google Scholar 

  44. Gheissari N, Sebastian T, Hartley R (2006) Person Reidentification using spatiotemporal appearance. In: 2006 IEEE computer society conference on computer vision and pattern recognition - (CVPR'06), vol 2. IEEE, pp 1528–1535

    Google Scholar 

  45. Gong S, Cristani M, Loy CC, Hospedales TM (2014) The re-identification challenge. In: Person re-identification. Springer, London, pp 1–20

    Chapter  Google Scholar 

  46. Gosselin PH, Cord M (2008) Active learning methods for interactive image retrieval. IEEE Trans Image Process 17(7):1200–1211

    Article  MathSciNet  Google Scholar 

  47. Gou M, Karanam S, Liu W, Camps O, Radke RJ (2017) DukeMTMC4ReID: A large-scale multi-camera person re-identification dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 10–19

    Google Scholar 

  48. Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an Ensemble of Localized Features. In: European conference on computer vision. Springer, Berlin, Heidelberg, pp 262–262

    Google Scholar 

  49. Hamdoun O, Moutarde F, Stanciulescu B, Steux B (2008a) Interest points harvesting in video sequences for efficient person identification

  50. Hamdoun O, Moutarde F, Stanciulescu B, Steux B (2008b) Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: 2008 2nd ACM/IEEE international conference on distributed smart cameras (ICDSC). IEEE, pp 1–6

    Google Scholar 

  51. Hirzer M, Beleznai C, Roth P, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: Scandinavian conference on image analysis. Springer, Berlin Heidelberg, pp 91–102

    Chapter  Google Scholar 

  52. Hirzer M, Roth P, Bischof H (2012a) Person re-identification by efficient impostor-based metric learning. In: 2012 IEEE ninth international conference on advanced video and signal-based surveillance. IEEE, pp 203–208

    Chapter  Google Scholar 

  53. Hirzer M, Roth P, Köstinger M, Bischof H (2012b) Relaxed pairwise learned metric for person re-identification. In: European conference on computer vision. Springer, Berlin, Heidelberg, pp 780–793

    Google Scholar 

  54. Hou R, Ma B, Chang H, Gu X, Shan S, Chen X (2019) VRSTC: occlusion-free video person re-identification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 7176–7185

    Google Scholar 

  55. Hu W, Hu M, Zhou X, Tan T, Lou J, Maybank S (2006) Principal axis-based correspondence between multiple cameras for people tracking. IEEE Trans Pattern Anal Mach Intell 28(4):663–671

    Article  Google Scholar 

  56. Huang T, Russell S (1997) Object identification in a Bayesian context. IJCAI 97:1276–1282

    Google Scholar 

  57. Huang H, Li D, Zhang Z, Chen X, Huang K (2018) Adversarially occluded samples for person re-identification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 5098–5107

    Google Scholar 

  58. Huang Y, Zha Z-J, Fu X, Zhang W (2019) Illumination-invariant person re-identification. In: Proceedings of the 27th ACM international conference on multimedia, pp 365–373

    Chapter  Google Scholar 

  59. India TO Delhi: AAP government installs CCTV cameras at public places. (2019). https://timesofindiaindiatimescom/city/delhi/tender-process-started-for-1-5-lakh-more-cctv-cameras-in-delhi-manish-sisodia/articleshow/69914234cms Accessed 14 April 2020

  60. Iwashita Y, Baba R, Ogawara K, Kurazume R (2010) Person identification from Spatio-temporal 3D gait. In: 2010 international conference on emerging security technologies, pp 30–35

    Chapter  Google Scholar 

  61. Javed O, Shafique K, Shah M (2005) Appearance modeling for tracking in multiple non-overlapping cameras. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol 2. IEEE, pp 26–33

    Google Scholar 

  62. Jeong K, Jaynes C (2008) Object matching in disjoint cameras using a color transfer approach. Mach Vis Appl 19(5–6):443–455

    Article  MATH  Google Scholar 

  63. Jiang M, Leng B, Song G, Meng Z (2020) Weighted triple-sequence loss for video-based person re-identification. Neurocomputing 381:314–321

    Article  Google Scholar 

  64. Karanam S, Li Y, Radke RJ (2015) Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: Proceeding of IEEE international conference on computer vision (ICCV). IEEE, pp 4516–4524

    Google Scholar 

  65. Karanam S, Gou M, Wu Z, Rates-Borras A, Camps O, Radke RJ (2018) A systematic evaluation and benchmark for person re-identification: features, metrics, and datasets. IEEE Trans Pattern Anal Mach Intell 41(3):523–536

    Article  Google Scholar 

  66. Kawai R, Makihara Y, Hua C, Iwama H, Yagi Y (2012) Person re-identification using view-dependent score-level fusion of gait and color features. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012). IEEE, pp 2694–2697

    Google Scholar 

  67. Khan A, Zhang J, Wang Y (2010) Appearance-based re-identification of people in video. In: 2010 international conference on digital image computing: techniques and applications. IEEE, pp 357–362

    Chapter  Google Scholar 

  68. Khedher MI, El-Yacoubi MA, Dorizzi B (2012) Probabilistic matching pair selection for surf-based person re-identification. In: 2012 BIOSIG-proceedings of the international conference of biometrics special interest group (BIOSIG). IEEE, pp 1–6

    Google Scholar 

  69. Kodirov E, Xiang T, Gong S (2015) Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification. In: BMVC, vol 3, p 8

    Google Scholar 

  70. Koestinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 2288–2295

    Chapter  Google Scholar 

  71. Lantagne M, Parizeau M, Bergevin R (2003) VIP: vision tool for comparing images of people. In: Proceedings of the 16th IEEE Conf. on vision Interface, pp 35–42

    Google Scholar 

  72. Layne R, Hospedales T, Gong S (2012) Person re-identification by attributes. In: Procedings of the British machine vision conference 2012. British Machine Vision Association, pp 24.1–24.11

    Google Scholar 

  73. Li W, Wang X (2013) Locally aligned feature transforms across views. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3594–3601

    Google Scholar 

  74. Li W, Zhao R, Wang X (2013) Human Reidentification with transferred metric learning. In: Asian conference on computer vision. Springer, Berlin, Heidelberg, pp 31–44

    Google Scholar 

  75. Li W, Zhao R, Xiao T, Wang X (2014) DeepReID: deep filter pairing neural network for person re-identification. In: Proceeding of IEEE conference on computer vision and pattern recognition, pp 152–159

    Google Scholar 

  76. Li X, Zheng WS, Wang X, Xiang T, Gong S (2015) Multi-scale learning for low-resolution person re-identification. In: Proceeding of IEEE international conference on computer vision (ICCV), pp 3765–3773

    Google Scholar 

  77. Li D, Chen X, Zhang Z, Huang K (2017a) Learning deep context-aware features over body and latent parts for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 384–393

    Google Scholar 

  78. Li, W., Zhu, X., & Gong, S. (2017b). Person re-identification by deep joint learning of multi-loss classification. arXiv preprint arXiv:1705.04724.

  79. Li M, Zhu X, Gong S (2018a) Unsupervised person re-identification by deep learning tracklet association. In: Proceedings of the European conference on computer vision (ECCV), pp 737–753

    Google Scholar 

  80. Li W, Zhu X, Gong S (2018b) Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285–2294

    Google Scholar 

  81. Li X, Wu A, Zheng WS (2018c) Adversarial open-world person re-identification. In: Proceeding of European conference on computer vision (ECCV), pp 280–296

    Google Scholar 

  82. Li R, Zhang B, Teng Z, Fan J (2021) A divide-and-unite deep network for person re-identification. Appl Intell 51(3):1479–1491

    Article  Google Scholar 

  83. Liao S, Li S (2015) Efficient psd constrained asymmetric metric learning for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 3685–3693

    Google Scholar 

  84. Liao S, Mo Z, Zhu J, Hu Y, Li SZ (2014) Open-set person re-identification. arXiv preprint arXiv:1408.0872

  85. Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2197–2206

    Google Scholar 

  86. Lin S, Li H, Li CT, Kot AC (2018) Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification. arXiv preprint arXiv:1807.01440

  87. Lin Y, Dong X, Zheng L, Yan Y, Yang Y (2019) A bottom-up clustering approach to unsupervised person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8738–8745

    Google Scholar 

  88. Lisanti G, Masi I, Bagdanov A, D. & Del Bimbo, A. (2014) Person re-identification by iterative re-weighted sparse ranking. IEEE Trans Pattern Anal Mach Intell 37(8):1629–1642

    Article  Google Scholar 

  89. Liu G, Wu J (2021) Video-based person re-identification by intra-frame and inter-frame graph neural network. Image Vis Comput 106:104068

    Article  Google Scholar 

  90. Liu C, Gong S, Loy CC, Lin X (2012) Person re-identification: what features are important? In: European conference on computer vision. Springer, Berlin, Heidelberg, pp 391–401

    Google Scholar 

  91. Liu Z, Zhang Z, Wu Q, Wang Y (2015) Enhancing person re-identification by integrating gait biometric. Neurocomputing 168:1144–1156

    Article  Google Scholar 

  92. Liu H, Xiao Z, Fan B, Zeng H, Zhang Y, Jiang G (2021) PrGCN: probability prediction with graph convolutional network for person re-identification. Neurocomputing 423:57–70

    Article  Google Scholar 

  93. Loy C, Liu C, Gong S (2013) Person re-identification by manifold ranking. In: 2013 IEEE international conference on image processing. IEEE, pp 3567–3571

    Chapter  Google Scholar 

  94. Ma B, Su Y, Jurie F (2012) Local descriptors encoded by fisher vectors for person re-identification. In: European conference on computer vision. Springer, Berlin, Heidelberg, pp 413–422

    Google Scholar 

  95. Ma L, Yang X, Tao D (2014) Person re-identification over camera networks using multi-task distance metric learning. IEEE Trans Image Process 23(8):3656–3670

    Article  MathSciNet  MATH  Google Scholar 

  96. Martinel N, Das A, Micheloni C, Roy-Chowdhury A (2016) Temporal model adaptation for person re-identification. In: European conference on computer vision. Springer, Cham, pp 858–877

    Google Scholar 

  97. Mazzon R, Tahir S, Cavallaro A (2012) Person re-identification in crowd. Pattern Recogn Lett 33(14):1828–1837

    Article  Google Scholar 

  98. Mignon A, Jurie F (2012) PCCA: A new approach for distance learning from sparse pairwise constraints. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 2666–2672

    Google Scholar 

  99. Nambiar AM, Bernardino A, Nascimento JC, Fred AL (2017) Towards view-point invariant person re-identification via fusion of anthropometric and gait features from Kinect measurements. In: VISIGRAPP (5: VISAPP), pp 108–119

    Google Scholar 

  100. Oreifej O, Mehran R, Shah M (2010) Human identity recognition in aerial images. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 709–716

    Chapter  Google Scholar 

  101. Paisitkriangkrai S, Shen C, Van Den Hengel A (2015) Learning to rank in person re-identification with metric ensembles. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1846–1855

    Google Scholar 

  102. Park U, Jain A, Kitahara I, Kogure K, Hagita N (2006) ViSE: visual search engine using multiple networked cameras. In: 18th international conference on pattern recognition (ICPR'06), vol 3. IEEE, pp 1204–1207

    Chapter  Google Scholar 

  103. Porikli F (2003) Inter-camera color calibration by correlation model function. In: Proceedings 2003 international conference on image processing (cat. No. 03CH37429), vol 2. IEEE, pp II–133

    Chapter  Google Scholar 

  104. Prosser, B. J., Gong, S., & Xiang, T. (2008). Multi-camera matching using bi-directional cumulative brightness transfer functions. In BMVC (Vol. 8, no. 164, p. 74).

  105. Prosser, B. J., Zheng, W. S., Gong, S., Xiang, T. (2010). Person re-identification by support vector ranking. In BMVC (Vol. 2, No. 5, p. 6).

  106. Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European conference on computer vision. Springer, Cham, pp 17–35

    Google Scholar 

  107. Roy A, Sural S, Mukherjee J (2012) A hierarchical method combining gait and phase of motion with spatiotemporal model for person re-identification. Pattern Recogn Lett 33(14):1891–1901

    Article  Google Scholar 

  108. Salve SG, Jondhale KC (2010) Shape matching and object recognition using shape contexts. In: 2010 3rd international conference on computer science and information technology, vol 9. IEEE, pp 471–474

    Chapter  Google Scholar 

  109. Sarfraz M, Schumann A, Eberle A, Stiefelhagen R (2018) A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 420–429

    Google Scholar 

  110. Satta R, Fumera G, Roli F, Cristani M, Murino V (2011) A multiple component matching framework for person re-identification. In: International conference on image analysis and processing. Springer, Berlin, Heidelberg, pp 140–149

    Google Scholar 

  111. Shao H, Wu Y, Cui W, Zhang J (2008) Image retrieval based on MPEG-7 dominant color descriptor. In: 2008 9th international conference for young computer scientists, pp 753–757

    Chapter  Google Scholar 

  112. Sheshkal SA, Fouladi-Ghaleh K, Aghababa H (2020) An improved person re-identification method by light-weight convolutional neural network. In: 2020 10th international conference on computer and knowledge engineering (ICCKE), pp 463–468

    Chapter  Google Scholar 

  113. Sun Y, Zheng L, Deng W, Wang S (2017) Svdnet for pedestrian retrieval. In: Proceedings of the IEEE international conference on computer vision, pp 3800–3808

    Google Scholar 

  114. Tao D, Guo Y, Song M, Li Y, Yu Z, Tang Y (2016) Person re-identification by dual-regularized KISS metric learning. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 25(6):2726–2738

    Article  MathSciNet  MATH  Google Scholar 

  115. Varior R, Haloi M, Wang G (2016) Gated Siamese convolutional neural network architecture for human re-identification. In: European conference on computer vision, vol 9912. Springer, Cham, pp 791–808

    Google Scholar 

  116. Vezzani R, Baltieri D, Cucchiara R (2013) People Reidentification in surveillance and forensics: A survey. ACM Computing Surveys (CSUR) 46(2):1–37

    Article  Google Scholar 

  117. Wang X (2013) Intelligent multi-camera video surveillance: A review. Pattern Recogn Lett 34(1):3–19

    Article  Google Scholar 

  118. Wang X, Zhao R (2014) Person re-identification: system design and evaluation overview. In: Person re-identification. Springer, London, pp 351–370

    Chapter  Google Scholar 

  119. Wang X, Doretto G, Sebastian T, Rittscher J, Tu P (2007) Shape and appearance context modeling. In: Proceedings of 11th international conference on computer vision. IEEE, pp 1–8

    Google Scholar 

  120. Wang T, Gong S, Zhu X, Wang S (2014) Person re-identification by video ranking. In: Proceeding of European conference on computer vision. Springer, pp 688–703

    Google Scholar 

  121. Wang T, Gong S, Zhu X, Wang S (2016) Person re-identification by discriminative selection in video ranking. IEEE Trans Pattern Anal Mach Intell 38(12):2501–2514

    Article  Google Scholar 

  122. Wang C, Zhang Q, Huang C, Liu W, Wang X (2018a) Mancs: A multi-task attentional network with curriculum sampling for person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 365–381

    Google Scholar 

  123. Wang J, Zhu X, Gong S, Li W (2018b) Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2275–2284

    Google Scholar 

  124. Wang Y, Wang L, You Y, Zou X, Chen V, Li S, … Weinberger KQ (2018c) Resource aware person re-identification across multiple resolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8042–8051

    Google Scholar 

  125. Wei L, Zhang S, Gao W, Tian Q (2018) Person transfer Gan to bridge domain gap for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 79–88

    Google Scholar 

  126. Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10(2)

  127. Wong KM, Po LM, Cheung KW (2006) Dominant color structure descriptor for image retrieval. In: 2007 IEEE international conference on image processing, vol 6. IEEE, pp VI–365

    Google Scholar 

  128. Wu S, Chen Y-C, Li X, Wu A-C, You J-J, Zheng W-S (2016) An enhanced deep feature representation for person re-identification. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 1–8

    Google Scholar 

  129. Wu L, Wang Y, Yin H, Wang M, Shao L (2019) Few-shot deep adversarial learning for video-based person re-identification. IEEE Trans Image Process 29:1233–1245

    Article  MathSciNet  Google Scholar 

  130. Wu Y, Bourahla OEF, Li X, Wu F, Tian Q, Zhou X (2020) Adaptive graph representation learning for video person re-identification. IEEE Trans Image Process 29:8821–8830

    Article  Google Scholar 

  131. XIA KG, Chang TIAN, ZENG MY (2018) Person re-identification robustness research on XQDA. In: DEStech transactions on computer science and engineering, (cnai)

    Google Scholar 

  132. Xiang JP (2012) Active learning for person re-identification. In: 2012 international conference on machine learning and cybernetics, vol 1. IEEE, pp 336–340

    Chapter  Google Scholar 

  133. Xiang Z, Chen Q, Liu Y (2014) Person re-identification by fuzzy space color histogram. Multimed Tools Appl 73(1):91–107

    Article  Google Scholar 

  134. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1249–1258

    Google Scholar 

  135. Xu D, Zheng H (2013) Person re-identification by multi-resolution saliency-weighted color histograms and local structural sparse coding. In: 2013 seventh international conference on image and graphics. IEEE, pp 477–482

    Chapter  Google Scholar 

  136. Xu Y, Ma B, Huang R, Lin L (2014) Person search in a scene by jointly modeling people commonness and person uniqueness. In: Proceedings of the 22nd ACM international conference on multimedia, pp 937–940

    Chapter  Google Scholar 

  137. Xun Y, Zhou P, Wang M (2018) Person reidentification via structural deep metric learning. IEEE Transactions on Neural Networks and Learning Systems 30(10):2987–2998

    Google Scholar 

  138. Yi D, Lei Z, Liao S, Li SZ (2014) Deep metric learning for person re-identification. In: 2014 22nd international conference on pattern recognition. IEEE, pp 34–39

    Chapter  Google Scholar 

  139. Yoon K, Harwood D, Davis L (2006) Appearance-based person recognition using color/path-length profile. J Vis Commun Image Represent 17(3):605–622

    Article  Google Scholar 

  140. Yu Y, Harwood D, Yoon K, Davis L (2007) Human appearance modeling for matching across video sequences. Mach Vis Appl 18(3–4):139–149

    Article  MATH  Google Scholar 

  141. Yu H, Wu A, Zheng W (2020) Unsupervised person re-identification by deep asymmetric metric embedding. IEEE Trans Pattern Anal Mach Intell 42(4):956–973

    Article  Google Scholar 

  142. Zajdel W, Zivkovic Z, Krose BJA (2005) Keeping track of humans: have I seen this person before? In: Proceedings of the 2005 IEEE international conference on robotics and automation, pp 2081–2086

    Chapter  Google Scholar 

  143. Zeng WS, Gong S, Xiang T (2009) Associating groups of people. In: Proceedings of the British machine vision conference, pp 23–21

    Google Scholar 

  144. Zhang, Y., & Li, S. (2011). Gabor-LBP based region covariance descriptor for person re-identification. In 2011 sixth international conference on image and graphics, (pp. 368-371).IEEE.

  145. Zhang R, Li J, Sun H, Ge Y, Luo P, Wang X, Lin L (2019) Scan: self-and-collaborative attention network for video person re-identification. IEEE Trans Image Process 28(10):4870–4882

    Article  MathSciNet  MATH  Google Scholar 

  146. Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3586–3593

    Google Scholar 

  147. Zhao H, Tian M, Sun S, Shao J, Yan J, Yi S, … Tang X (2017) Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1077–1085

    Google Scholar 

  148. Zheng W, Gong S, Xiang T (2011) Person re-identification by probabilistic relative distance comparison. In: CVPR 2011. IEEE, pp 649–656

    Chapter  Google Scholar 

  149. Zheng W, Gong S, Xiang T (2013) Reidentification by relative distance comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653–668

    Article  Google Scholar 

  150. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: A benchmark. In: Proceedings of the IEEE international conference on computer vision, pp 1116–1124

    Google Scholar 

  151. Zheng L, Yang Y, Hauptmann AG (2016a) Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984

  152. Zheng W, Gong S, Xiang T (2016b) Towards open-world person re-identification by one-shot group-based verification. IEEE Trans Pattern Anal Mach Intell 38(3):591–606

    Article  Google Scholar 

  153. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by Gan improve the person re-identification baseline in vitro. In: Proceedings of the IEEE international conference on computer vision, pp 3754–3762

    Google Scholar 

  154. Zheng M, Karanam S, Radke R (2018) Rpifield: A new dataset for temporally evaluating person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1893–1895

    Google Scholar 

  155. Zhong Z, Zheng L, Li S, Yang Y (2018) Generalizing a person retrieval model hetero-and homogeneously. In: Proceedings of the European conference on computer vision (ECCV), pp 172–188

    Google Scholar 

  156. Zhu X, Jing X, You X, Zhang X, Zhang T (2018) Video-based person re-identification by simultaneously learning intra-video and inter-video distance metrics. IEEE Trans Image Process 27(11):5683–5695

    Article  MathSciNet  MATH  Google Scholar 

  157. Zhu, X., Zhu, X., Li, M., Morerio, P., Murino, V., & Gong, S. (2021). Intra-camera supervised person re-identification. International journal of computer vision, 1–16.

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Singh, N.K., Khare, M. & Jethva, H.B. A comprehensive survey on person re-identification approaches: various aspects. Multimed Tools Appl 81, 15747–15791 (2022). https://doi.org/10.1007/s11042-022-12585-w

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