logo

SCIENTIA SINICA Informationis, Volume 48 , Issue 5 : 545-563(2018) https://doi.org/10.1360/N112018-00017

Asymmetric person re-identification: cross-view person tracking in a large camera network

More info
  • ReceivedJan 17, 2018
  • AcceptedMar 27, 2018
  • PublishedMay 14, 2018

Abstract


Funded by

国家自然科学基金(61522115)


Acknowledgment

作者感谢如下几位同学的协助: 俞洪兴、叫洁宁、银舟、孟静珂.


References

[1] Collins R T, Lipton A J, Kanade T. Introduction to the special section on video surveillance. IEEE Trans Pattern Anal Mach Intel, 2000, 22: 745-746 CrossRef Google Scholar

[2] Xiang T, Gong S G. Video behavior profiling for anomaly detection. IEEE Trans Pattern Anal Mach Intel, 2008, 30: 893-908 CrossRef PubMed Google Scholar

[3] Wang L, Hu W M, Tan T N. A survey of visual analysis of human motion. Chin J Comput, 2002, 25: 225--237. Google Scholar

[4] Gheissari N, Sebastian T B, Hartley R. Person reidentification using spatiotemporal appearance. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), New York, 2006. 1528--1535. Google Scholar

[5] Gray D, Tao H. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Proceedings of European Conference on Computer Vision (ECCV), Marseille, 2008. 262--275. Google Scholar

[6] Swain M J, Ballard D H. Color indexing. Int J Comput Vision, 1991, 7: 11-32 CrossRef Google Scholar

[7] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intel, 2002, 24: 971-987 CrossRef Google Scholar

[8] Fogel I, Sagi D. Gabor filters as texture discriminator. Biol Cybern, 1989, 61: 103-113 CrossRef Google Scholar

[9] Wang X G, Doretto G, Sebastian T, et al. Shape and appearance context modeling. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Rio de Janeiro, 2007. 1--8. Google Scholar

[10] Farenzena M, Bazzani L, Perina A, et al. Person re-identification by symmetry-driven accumulation of local features. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), San Francisco, 2010. 2360--2367. Google Scholar

[11] Ma B P, Su Y, Jurie F. Local descriptors encoded by fisher vectors for person re-identification. In: Proceedings of European Conference on Computer Vision Workshop (ECCV), Florence, 2012. 413--422. Google Scholar

[12] Kviatkovsky I, Adam A, Rivlin E. Color invariants for person reidentification. IEEE Trans Pattern Anal Mach Intel, 2013, 35: 1622-1634 CrossRef PubMed Google Scholar

[13] Ma B P, Su Y, Jurie F. Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vision Comput, 2014, 32: 379-390 CrossRef Google Scholar

[14] Li W, Wang X G. Locally aligned feature transforms across views. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, 2013. 3594--3601. Google Scholar

[15] Layne R, Hospedales T M, Gong S G. Towards person identification and re-identification with attributes. In: Proceedings of European Conference on Computer Vision (ECCV), Florence, 2012. 402--412. Google Scholar

[16] Cheng D S, Cristani M, Stoppa M, et al. Custom pictorial structures for re-identification. In: Proceedings of British Machine Vision Conference (BMVC), Dundee, 2011. Google Scholar

[17] Wu Z Y, Li Y, Radke R J. Viewpoint invariant human re-identification in camera networks using pose priors and subject-discriminative features. IEEE Trans Pattern Anal Mach Intel, 2015, 37: 1095-1108 CrossRef PubMed Google Scholar

[18] Liao S C, Hu Y, Zhu X Y, et al. Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015. 2197--2206. Google Scholar

[19] Matsukawa T, Okabe T, Suzuki E, et al. Hierarchical gaussian descriptor for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016. 1363--1372. Google Scholar

[20] Zhao R, Ouyang W L, Wang X G. Learning mid-level filters for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, 2014. 144--151. Google Scholar

[21] Chen T C, Zheng W S, Lai J H. Mirror representation for modeling view-specific transform in person re-identification. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, 2015. 3402--3408. Google Scholar

[22] Yang Y, Yang J M, Yan J J, et al. Salient color names for person re-identification. In: Proceedings of European Conference on Computer Vision (ECCV), Zurich, 2014. 536--551. Google Scholar

[23] Su C, Yang F, Zhang S L, et al. Multi-task learning with low rank attribute embedding for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, 2015. 3739--3747. Google Scholar

[24] Su C, Zhang S L, Xing J L, et al. Deep attributes driven multi-camera person re-identification. In: Proceedings of European Conference on Computer Vision (ECCV), Amsterdam, 2016. 475--491. Google Scholar

[25] Li W, Zhao R, Xiao T, et al. DeepreID: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, 2014. 152--159. Google Scholar

[26] Wu S X, Chen Y C, Li X, et al. An enhanced deep feature representation for person re-identification. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, 2016. 1--8. Google Scholar

[27] Ahmed E, Jones M, Marks T K. An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015. 3908--3916. Google Scholar

[28] Xiao T, Li H S, Ouyang W L, et al. Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016. 1249--1258. Google Scholar

[29] Zhao L M, Li X, Wang J D, et al. Deeply-learned part-aligned representations for person re-identification. 2017,. arXiv Google Scholar

[30] Li D W, Chen X T, Zhang Z, et al. 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 (CVPR), Las Vegas, 2017. 384--393. Google Scholar

[31] Zhao H Y, Tian M Q, Sun S Y, et al. 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 (CVPR), Honolulu, 2017. 1077--1085. Google Scholar

[32] Barbosa I B, Cristani M, Bue A D, et al. Re-identification with RGB-D sensors. In: Proceedings of European Conference on Computer Vision (ECCV), Florence, 2012. 433--442. Google Scholar

[33] Munaro M, Basso A, Fossati A, et al. 3D reconstruction of freely moving persons for re-identification with a depth sensor. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 2014. 4512--4519. Google Scholar

[34] Takac B, Catala A, Rauterberg M, et al. People identification for domestic non-overlapping rgb-d camera networks. In: Proceedings of International Multi-Conference on Systems, Signals and Devices (SSD), Barcelona, 2014. Google Scholar

[35] Oliver J, Albiol A, Albiol A. 3D descriptor for people re-identification. In: Proceedings of International Conference on Pattern Recognition (ICPR), Tsukuba, 2012. 1395--1398. Google Scholar

[36] Haque A, Alahi A, Li F F. Recurrent attention models for depth-based person identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016. 1229--1238. Google Scholar

[37] Wu A C, Zheng W S, Lai J H. Robust depth-based person re-identification. IEEE Trans Image Process, 2017, 26: 2588-2603 CrossRef PubMed ADS arXiv Google Scholar

[38] Ren L L, Lu J W, Feng J J. Multi-modal uniform deep learning for RGB-D person re-identification. Pattern Recogn, 2017, 72: 446-457 CrossRef Google Scholar

[39] Prosser B J, Zheng W S, Gong S G, et al. Person re-identification by support vector ranking. In: Proceedings of British Machine Vision Conference (BMVC), Aberystwyth, 2010. Google Scholar

[40] Zheng W S, Gong S G, Xiang T. Reidentification by relative distance comparison. IEEE Trans Pattern Anal Mach Intel, 2013, 35: 653-668 CrossRef PubMed Google Scholar

[41] Köstinger M, Hirzer M, Wohlhart P, et al. Large scale metric learning from equivalence constraints. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, 2012. 2288--2295. Google Scholar

[42] Tao D P, Jin L W, Wang Y F. Person re-identification by regularized smoothing kiss metric learning. IEEE Trans Circ Syst Video Technol, 2013, 23: 1675-1685 CrossRef Google Scholar

[43] Tao D P, Jin L W, Wang Y F. Person reidentification by minimum classification error-based KISS metric learning. IEEE Trans Cybernet, 2015, 45: 242-252 CrossRef PubMed Google Scholar

[44] Pedagadi S, Orwell J, Velastin S, et al. Local fisher discriminant analysis for pedestrian re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, 2013. 3318--3325. Google Scholar

[45] Mignon A, Jurie F. Pcca: a new approach for distance learning from sparse pairwise constraints. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, 2012. 2666--2672. Google Scholar

[46] Xu Y L, Lin L, Zheng W S, et al. Human re-identification by matching compositional template with cluster sampling. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, 2013. 3152--3159. Google Scholar

[47] Li Z, Chang S Y, Liang F, et al. Learning locally-adaptive decision functions for person verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, 2013. 3610--3617. Google Scholar

[48] Ma L Y, Yang X K, Tao D C. Person re-identification over camera networks using multi-task distance metric learning. IEEE Trans Image Process, 2014, 23: 3656-3670 CrossRef PubMed ADS Google Scholar

[49] Lisanti G, Masi I, Bagdanov A D. Person re-identification by iterative re-weighted sparse ranking. IEEE Trans Pattern Anal Mach Intel, 2015, 37: 1629-1642 CrossRef PubMed Google Scholar

[50] Zhao R, Ouyang W L, Wang X G. Unsupervised salience learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, 2013. 3586--3593. Google Scholar

[51] Zhao R, Ouyang W L, Wang X G. Person re-identification by salience matching. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, 2013. 2528--2535. Google Scholar

[52] Zhao R, Oyang W L, Wang X G. Person re-identification by saliency learning. IEEE Trans Pattern Anal Mach Intel, 2017, 39: 356-370 CrossRef PubMed Google Scholar

[53] Liu X, Song M L, Tao D C, et al. Semi-supervised coupled dictionary learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, 2014.łinebreak 3550--3557. Google Scholar

[54] Xiong F, Gou M R, Camps O, et al. Person re-identification using kernel-based metric learning methods. In: Proceedings of European Conference on Computer Vision (ECCV), Zurich, 2014. 1--16. Google Scholar

[55] Yi D, Lei Z, Liao S C, et al. Deep metric learning for person re-identification. In: Proceedings of International Conference on Pattern Recognition (ICPR), Stockholm, 2014. 34--39. Google Scholar

[56] Karanam S, Li Y, Radke R J. Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, 2015. 4516--4524. Google Scholar

[57] Liao S C, Li S Z. Efficient PSD constrained asymmetric metric learning for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, 2015. 3685--3693. Google Scholar

[58] Shen Y, Lin W Y, Yan J C, et al. Person re-identification with correspondence structure learning. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, 2015. 3200--3208. Google Scholar

[59] Garcia J, Martinel N, Micheloni C, et al. Person re-identification ranking optimisation by discriminant context information analysis. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, 2015. 1305--1313. Google Scholar

[60] Chen Y C, Zheng W S, Lai J H. An asymmetric distance model for cross-view feature mapping in person re-identification. IEEE Trans Circ Syst Video Technol, 2017, 27: 1661-1675 CrossRef Google Scholar

[61] Chen W H, Chen X T, Zhang J G, et al. Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017. Google Scholar

[62] Kodirov E, Xiang T, Fu Z Y, et al. Person re-identification by unsupervised $\ell_1$ graph learning. In: Proceedings of European Conference on Computer Vision (ECCV), Amsterdam, 2016. 178--195. Google Scholar

[63] Peng P X, Xiang T, Wang Y W, et al. Unsupervised cross-dataset transfer learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016. 1306--1315. Google Scholar

[64] You J J, Wu A C, Li X, et al. Top-push video-based person re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016. 1345--1353. Google Scholar

[65] Wang T Q, Gong S G, Zhu X T, et al. Person re-identification by video ranking. In: Proceedings of European Conference on Computer Vision (ECCV), Zurich, 2014. 688--703. Google Scholar

[66] Wang T Q, Gong S G, Zhu X T. Person re-identification by discriminative selection in video ranking. IEEE Trans Pattern Anal Mach Intel, 2016, 38: 2501-2514 CrossRef PubMed Google Scholar

[67] McLaughlin N, Rincon J M, Miller P. Recurrent convolutional network for video-based person re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016. 1325--1334. Google Scholar

[68] Zhu X K, Jing X Y, Wu F, et al. Video-based person re-identification by simultaneously learning intra-video and inter-video distance metrics. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), New York, 2016. 3552--3558. Google Scholar

[69] Zhou Z, Huang Y, Wang W, et al. See the forest for the trees: Joint spatial and temporal recurrent neural networks for video-based person re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017. 6776--6785. Google Scholar

[70] Zhang W, Yu X D, He X Y. Learning bidirectional temporal cues for video-based person re-identification. IEEE Trans Circ Syst Video Technol, 2017, CrossRef Google Scholar

[71] Chung D, Tahboub K, Delp E J. A two stream siamese convolutional neural network for person re-identification. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Venice, 2017. 1992--2000. Google Scholar

[72] Wang X J, Zheng W S, Li X. Cross-scenario transfer person re-identification. IEEE Trans Circuits Syst Video Technol, 2016, 26: 1447-1460 CrossRef Google Scholar

[73] Li W, Zhao R, Wang W G. Human reidentification with transferred metric learning. In: Proceedings of Asian Conference on Computer Vision (ACCV), Daejeon, 2012. 31--44. Google Scholar

[74] Ma A J, Yuen P C, Li J. Domain transfer support vector ranking for person re-identification without target camera label information. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Sydney, 2013. 3567--3574. Google Scholar

[75] Jiao J N, Zheng W S, Wu A C, et al. Deep low-resolution person re-identification. In: Proceedings of Association for the Advancement of Artificial Intelligence (AAAI), New Orleans, 2018. Google Scholar

[76] Li X, Zheng W S, Wang X J, et al. Multi-scale learning for low-resolution person re-identification. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Santiago, 2015. 3765--3773. Google Scholar

[77] Jing X Y, Zhu X, Wu F. Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning. IEEE Trans Image Process, 2017, 26: 1363-1378 CrossRef PubMed ADS Google Scholar

[78] Wang Z, Hu R M, Yu Y, et al. Scale-adaptive low-resolution person re-identification via learning a discriminating surface. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), New York, 2016.łinebreak 2669--2675. Google Scholar

[79] Zheng W S, Gong S G, Xiang T. Associating groups of people. In: Proceedings of British Machine Vision Conference (BMVC), London, 2009. Google Scholar

[80] Zheng W S, Gong S, Xiang T. Transfer re-identification: from person to set-based verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, 2012. 2650--2657. Google Scholar

[81] Zheng W S, Gong S, Xiang T. Towards open-world person re-identification by one-shot group-based verification. IEEE Trans Pattern Anal Mach Intel, 2016, 38: 591-606 CrossRef PubMed Google Scholar

[82] Li S, Xiao T, Li H S, et al. Person search with natural language description. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017. 5187--5196. Google Scholar

[83] Xiao T, Li S, Wang B C, et al. Joint detection and identification feature learning for person search. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017. 3376--3385. Google Scholar

[84] Chen Y C, Zheng W S, Lai J H. An asymmetric distance model for cross-view feature mapping in person re-identification. IEEE Trans Circ Syst Video Technol, 2017, 27: 1661-1675 CrossRef Google Scholar

[85] Chen Y C, Zhu X T, Zheng W S. Person re-identification by camera correlation aware feature augmentation. IEEE Trans Pattern Anal Mach Intel, 2018, 40: 392-408 CrossRef PubMed Google Scholar

[86] Zhu X, Wu B, Huang D. Fast open-world person re-identification. IEEE Trans Image Process, 2018, 27: 2286-2300 CrossRef PubMed ADS Google Scholar

[87] Wu A, Zheng W S, Yu H X, et al. Rgb-infrared cross-modality person re-identification. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Venice, 2017. 5390--5399. Google Scholar

[88] Yin Z, Zheng W S, Wu A C, et al. Learning a semantically discriminative joint space for attribute based person re-identification. 2017,. arXiv Google Scholar

[89] Yu H X, Wu A, Zheng W S. Cross-view asymmetric metric learning for unsupervised person re-identification. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Venice, 2017. 994--1002. Google Scholar

[90] Zheng W S, Li X, Xiang T, et al. Partial person re-identification. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), Santiago, 2015. 4678--4686. Google Scholar