[1]SONG Wanru,ZHAO Qingqing,CHEN Changhong,et al.Survey on pedestrian re-identification research[J].CAAI Transactions on Intelligent Systems,2017,12(6):770-780.[doi:10.11992/tis.201706084]
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Survey on pedestrian re-identification research

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 6 Page number: 770-780 Column: 综述 Public date: 2017-12-25
Title:
Survey on pedestrian re-identification research
Author(s):
SONG Wanru;  ZHAO Qingqing;  CHEN Changhong;  GAN Zongliang;  LIU Feng
College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Keywords:
pedestrian re-identification; feature representation; metric learning; deep learning; convolutional neural networks; datasets; video surveillance
CLC:
TP181
DOI:
10.11992/tis.201706084
Abstract:
The intelligent video analysis method based on pedestrian re-identification has become a research focus in the field of computer vision, and it has received extensive attention from the academic community. Pedestrian re-identification aims to verify pedestrian identity in image sequences captured by cameras that are orientated in different directions at different times. This current study is classified into two categories: image-based and video-based algorithms. For these two categories, using feature description, metric learning, and various benchmark datasets, detailed analysis is performed, and a summary is presented. In addition, the wide application of deep-learning algorithms in recent years has changed pedestrian re-identification in terms of feature description and metric learning. The paper summarizes the application of deep learning in pedestrian re-identification and looks at future development trends.
References:
[1] LI Y, WU Z, KARANAM S, et al. Real-world re-identification in an airport camera network[C]//International Conference on Distributed Smart Cameras. Venice, Italy, 2014: 35.
[2] GONG S, CRISTANI M, YAN S, et al. Person re-identification [M]. London, UK: Springer, 2014.
[3] CAMPS O, GOU M, HEBBLE T, et al. From the lab to the real world: Re-identification in an airport camera network[J]. IEEE transactions on circuits and systems for video technology, 2016, (99): 540-553.
[4] GRAY D, TAO H. Viewpoint invariant pedestrian recognition with an ensemble of localized features[C]//European Conference on Computer Vision. Marseill, France, 2008: 262-275.
[5] PROSSER B, ZHENG W S, GONG S, et al. Person re-identification by support vector ranking[C]//The British Machine Vision Conference. Aberystwyth, British, 2010: 1-21.
[6] JURIE F, MIGNON A. PCCA: a new approach for distance learning from sparse pairwise constraints[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2012: 2666-2672.
[7] ZHAO R, OUYANG W, WANG X. Unsupervised salience learning for person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Oregon, USA, 2013: 3586-3593.
[8] ZHENG W S, LI X, XIANG T. Partial person re-identifi cation[C]//IEEE International Conference on Computer Vision. Santiago, Chile, 2015: 4678-4686.
[9] CAI Q, AGGARWAL J K. Tracking human motion using multiple cameras[C]//International Conference on Pattern Recognition. Vienna, Austria, 1996: 68-72.
[10] GHEISSARI N, SEBASTIAN T B, HARTLEY R. Person re-identification using spatiotemporal appearance[C]//IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, 2006: 1528-1535.
[11] GRAY D, BRENNAN S, TAO H. Evaluating appearance models for recognition, reacquisition, and tracking[J]. International journal of computer vision, 2007, 89(2): 56-68.
[12] GONG S G, CRISTANI M, YAN S C, et al. Person re-identification[J]. Advances in computer vision and pattern recognition, 2013, 42(7): 301-313.
[13] YI D, LEI Z, LI S Z, Deep metric learning for practical person re-identification[C]//International Conference on Pattern Recognition. Stockholm Waterfront, Sweden, 2014.
[14] OREOFEJ O, MEHRAN R, SHAH M. Human identity recognition in aerial images[C]//IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 709-716.
[15] JUNGLING K, BODENSTEINER C, ARENS M. Person re-identification in multi-camera networks[C]//Computer Vision and Pattern Recognition Workshops. Colorado, USA, 2010: 709-716.
[16] ZHENG W S, GONG S G, XIANG T. Re identification by relative distance comparison[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(3): 653.
[17] PEDAGADI S, ORWELL J, VELASTIN S, et al. Local fisher discriminant analysis for pedestrian re-identification [C]//IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 3318-3325.
[18] CHEN Y C, ZHENG W S, LAI J H, et al. An asymmetric distance model for cross-view feature mapping in person re-identification[J]. IEEE transactions on circuits and systems for video technology, 2016 (99): 1661-1675.
[19] XIONG F, GOU M, CAMPS O, et al. Person re-Identification using kernel-based metric learning methods[C]//European Conference on Computer Vision. Zurich, Switzerland, 2014:1-16.
[20] ZHENG L, SHEN L, TIAN L, et al. Scalable person re-identification: a benchmark[C]//IEEE International Conference on Computer Vision. Santiago, Chile, 2015; 1116-1124.
[21] WANG T, GONG S G, ZHU X, et al. Person re-identification by video ranking[C]//European Conference on Computer Vision. Zurich, Switzerland, 2014: 688-703.
[22] ZHENG L, BIE Z, SUN Y, et al. MARS: A video benchmark for large-scale person re-identification[M]//European Conference on Computer Vision. Springer International Publishing, 2016: 868-884.
[23] KLASER A, MARSZALEK M, SCHMID C. A spatio-temporal descriptor based on 3D-gradients[C]//British Machine Vision Conference 2008. Nottingham, British, 2008: 152-159.
[24] YOU J, WU A, LI X, et al. Top-push video-based person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Las vegas, USA, 2016: 1345-1353.
[25] ZHAO R, OUYANG W, WANG X R. Unsupervised salience learning for person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 3586-3593.
[26] BAK S, CORVEE E, BREMOND F, et al. Person re-identification using haar-based and DCD-based signature[C]//IEEE International Conference on Advanced Video and Signal Based Surveillance. Boston, USA, 2010: 1-8.
[27] KOESTINGER M, HIRZER M, WOHLHART P, et al. Large scale metric learning from equivalence constraint[C]//IEEE Conference on Computer Vision and Pattern Recognition. Providence, Rhode island, 2012: 2288-2295.
[28] ENGEL C, BAUMGARTNE P, HOLZMANN M, et al. Person re-identification by support vector ranking[C]//British Machine Vision Conference 2010. Aberystwyth, UK, 2010: 1-11.
[29] SCHWARTZ W R, DAVIS L S. Learning discriminative appearance-based models using partial least squares[C]//XXⅡ Brazilian Symposium on Computer Graphics and Image Processing. Gramado, Brazil, 2010: 322-329.
[30] LAYNE R, HOSPEDALES T M, GONG S G. Person Re-identification by Attributes[C]//The British Machine Vision Conference. Nottingham, Park, 2014, 2(3): 8.
[31] SHI Z, HOSPEDALSE T M, XIANG T. Transferring a semantic representation for person re-identification and search[C]//Computer Vision and Pattern Recognition. Boston, USA, 2015: 4184-4193.
[32] MA B, SU Y, JURIE F. Local descriptors encoded by fisher vectors for person re-identification[C]//International Conference on Computer Vision. Barcelona, Spain, 2012: 413-422.
[33] CHEN D, YUAN Z, HUA G, et al. Similarity learning on an explicit polynomial kernel feature map for person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015: 1565-1573.
[34] GOU M, ZHANG X, RATES-BORRAS A, et al. Person re-identification in appearance impaired scenarios[C]//British Machine Vision Conference. [S.l.], 2016: 1-48.
[35] KARANAM S, LI Y, RADKE R J. Person re-identification with discriminatively trained viewpoint invariant dictionaries[C]//IEEE International Conference on Computer Vision. Santiago, Chile, 2015: 4516-4524.
[36] SUGIYAMA, MASASHI. Local fisher discriminant analysis for supervised dimensionality reduction[J]. Machine learning, 2010, 78(1/2): 35-61.
[37] MATSUKAWA T, OKABE T, SUZUKI E, et al. Hierarchical gaussian descriptor for person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 1363-1372.
[38] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems. Doha, Qatar, 2012: 1097-1105.
[39] MCLAUGHLIN N, RINCON J M, MILLER P. Recurrent Convolutional Network for Video-based Person Re-Identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2012: 51-58.
[40] XIAO T, LI H, OUYANG W, et al. Learning deep feature representations with domain guided dropout for person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 1249-1258.
[41] XING E P, NG A Y, JORDAN M I, et al. Distance metric learning, with application to clustering with side-information[C]//International Conference on Neural Information Processing Systems. Vancouver: MIT Press, 2002: 521-528.
[42] WEINBERGER K Q, SAUL K L. Distance metric learning for large margin nearest neighbor classification[J]. Journal of machine learning research, 2009, 10(1): 207-244.
[43] DIKMEN M, AKBAS E, HUANG T S, et al. Pedestrian recognition with a learned metric[J]. Lecture notes in computer science, 2010, 6495: 501-512.
[44] GUILLAUMIN M, VERBEEK J, SCHMID C. Is that you? Metric learning approaches for face identification[C]//Proceedings of the 12th International Conference on Computer Vision. Kyoto, Japan, 2009: 498-505.
[45] ZHENG W, GONG S, XIANG T. Person re-identification by probabilistic relative distance comparison[C]//IEEE conference on Computer Vision and Pattern Recognition. Colorado Springs, USA, 2011: 649-656.
[46] ZHENG W S, GONG S, XIANG T. Re-identification by relative distance comparison[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(3): 653.
[47] LIAO S, HU Y, ZHU X, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015: 2197-2206.
[48] YI D, LEI Z, LI S Z. Deep metric learning for practical person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014: 34-39.
[49] LIU H, MA B, QIN L, et al. Set-label modeling and deep metric learning on person re-identification[J]//Neurocomputing, 2015 (151): 1283-1292.
[50] LI W, ZHAO R, XIAO T, et al. Deepreid: Deep filter pairing neural network for person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014: 152–159.
[51] DING S, LIN L, WANG G, et al. Deep feature learning with relative distance comparison for person re-identification[J]. Pattern recognition, 2015, 48(10): 2993-3003.
[52] ZHENG W S, GONG S, XIANG T. Associating groups of people[C]//Proceedings of the British Machine Vision Conference. London, UK, 2009: 251-259.
[53] CHEN C L, XIANG T, GONG S. Multi-camera activity correlation analysis[C]//IEEE conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 1988-1995.
[54] DONG S C, CRISTANI M, STOPPA M, et al. Custom pictorial structures for re-identification[C]//British Machine Vision Conference. Dundee, British. 2011: 159-165.
[55] LI W, ZHAO R, WANG X. Human re-identification with transferred metric learning[C]//Asian Conference on Computer Vision. Daejeon, Korea, Springer-Verlag, 2012: 31-44.
[56] LI W, WANG X. Locally aligned feature transforms across views[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 3594-3601.
[57] LI W, ZHAO R, XIAO T, et al. DeepReID: deep filter pairing neural network for person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014: 152-159.
[58] DAS A, CHAKRABORTY A, ROY-CHOWDHURY A K. Consistent re-identification in a camera network[C]//European Conference on Computer Vision. Springer International Publishing, 2014: 330-345.
[59] ROTH P M, HIRZER M, KOSTINGER M, et al. Mahalanobis distance learning for person re-identification[M]. London: Person re-identification, 2014: 247-267.
[60] SCOVANNER P, ALI S, SHAH M. A 3-dimensional sift descriptor and its application to action recognition[C]//15th ACM International Conference on Multimedia. New York, USA, 2007: 357-360.
[61] BEDAGKAR-GALA A, SHAH S K. Gait-assisted person re-identification in wide area surveillance[C]//Asian Conference on Computer Vision. Singapore: Springer International Publishing, 2014: 633-649.
[62] SIMONNET D, LEWANDOWSKI M, VELASTIN S A, et al. Re-identification of pedestrians in crowds using dynamic time warping[C]//International Conference on Computer Vision. Springer-Verlag, 2012: 423-432.
[63] MAN J,BHANU B. Individual recognition using gait energy image[J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28(2): 316-322.
[64] YAN Y, NI B, SONG Z, et al. Person Re-identification via recurrent feature aggregation[C]//European Conference on Computer Vision. Springer International Publishing, 2016: 701-716.
[65] ZHOU Z, HUANG Y, WANG W, et al. See the forest for the trees: joint spatial and temporal recurrent neural networks in video-based person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 143-147.
[66] LIU H, JIE Z, JAYASHREE K, et al. Video-based person re-identification with accumulative motion context[J]. IEEE transactions on circuits and systems for video technology, 2017, (99):23-29.
[67] ESS A, LEIBE B, GOOL L V. Depth and appearance for mobile scene analysis[C]//International Conference on Computer Vision. Rio de Janeiro, Brazil, 2007: 1-8.
[68] BALTIERI D, VEZZANI R, CUCCHIARA R. 3DPeS: 3D people dataset for surveillance and forensics[C]//Joint ACM Workshop on Human Gesture and Behavior Understanding. DOI: 10.1145/2072572.2072590.
[69] HIRZER M, BELEZNAI C, ROTH P M, et al. Person re-identification by descriptive and discriminative classification[C]//Scandinavian Conference on Image Analysis. Springer Berlin Heidelberg, 2011: 91-102.
[70] GARCIA J, MRTINEL N, MICHELONI C, et al. Person re-identification ranking optimisation by discriminant context information analysis[C]//IEEE International Conference on Computer Vision. Santiago, Chile, 2015: 1305-1313.
[71] VARIOR R R, HALOI M, WANG G. Gated siamese convolutional neural network architecture for human re-identification[C]//European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 791-808.
[72] GEMICI M, HUANG C, SANTORO A, et al. Generative temporal models with memory[J]. arXiv preprint arXiv: 1702.04649, 2017.
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