基于深度学习的行人重识别研究进展

罗浩; 姜伟; 范星; 张思朋; 罗浩; 姜伟; 范星; 张思朋

doi:10.16383/j.aas.c180154

[1]

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In:Proceedings of the 2014 Advances in Neural Information Processing Systems (NIPS). Montréal, Canada:MIT Press, 2014. 2672-2680

[2]

Zajdel W, Zivkovic Z, Krose B J A. Keeping track of humans:have I seen this person before? In:Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain:IEEE, 2005. 2081-2086

[3]

Zheng L, Yang Y, Hauptmann A G. Person re-identification:past, present and future, arXiv preprint arXiv:1610.02984, 2016.

[4]

Dalal N, Triggs B. Histograms of oriented gradients for human detection. In:Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA:IEEE, 2005. 886-893

[5]

Lowe D G. Object recognition from local scale-invariant features. In:Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece:IEEE, 1999. 1150-1157

[6]

Köstinger M, Hirzer M, Wohlhart P, Both P M, Bischof H. Large scale metric learning from equivalence constraints. In:Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA:IEEE, 2012. 2288-2295

[7]

Liao S C, Hu Y, Zhu X Y, Li S Z. Person re-identification by local maximal occurrence representation and metric learning. In:Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA:IEEE, 2015. 2197-2206

[8]

He K M, Zhang X Y, Ren S Q, Sun J. Delving deep into rectifiers:surpassing human-level performance on ImageNet classification. In:Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015. 1026-1034

[9]

Lu C C, Tang X O. Surpassing human-level face verification performance on LFW with Gaussian face. In:Proceedings of the 29th AAAI Conference on Artificial Intelligence. Austin, Texas, USA:AAAI, 2015. 3811-3819

[10]

Gray D, Brennan S, Tao H. Evaluating appearance models for recognition, reacquisition, and tracking. In:Proceedings of the 10th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS). Rio de Janeiro:IEEE, 2007. 1-7

[11]

Hirzer M, Beleznai C, Roth P M, Bischof H. Person re-identification by descriptive and discriminative classification. In:Proceedings of the 2011 Scandinavian Conference on Image Analysis. Berlin, Heidelberg:Springer, 2011. 91-102

[12]

Li W, Zhao R, Xiao T, Wang X G. DeepReID:deep filter pairing neural network for person re-identification. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE, 2014. 152-159

[13]

Zheng L, Shen L Y, Tian L, Wang S J, Wang J D, Tian Q. Scalable person re-identification:a benchmark. In:Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015. 1116-1124

[14]

Xiao T, Li S, Wang B C, Lin L, Wang X G. End-to-end deep learning for person search. arXiv preprint, arXiv:1604.01850, 2016.

[15]

Zheng L, Bie Z, Sun Y F, Wang J D, Su C, Wang S J, et al. Mars:a video benchmark for large-scale person re-identification. In:Proceedings of the 2016 European Conference on Computer Vision. Cham:Springer, 2016. 868-884

[16]

Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C. Performance measures and a data set for multi-target, multi-camera tracking. In:Proceedings of the 2016 European Conference on Computer Vision. Cham:Springer, 2016. 17-35

[17]

Wu A C, Zheng W S, Yu H X, Gong S G, Lai J H. RGB-infrared cross-modality person re-identification. In:Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017. 5390-5399

[18]

Song G, Leng B, Liu Y, Hetang C, Cai S F. Region-based quality estimation network for large-scale person re-identification. In:Proceedings of the 2018 Association for the Advancement of Artificial Intelligence. New Orleans:AAAI, 2018.

[19]

Wei L H, Zhang S L, Gao W, Tian Q. Person transfer GAN to bridge domain gap for person re-identification. arXiv:1711.08565, 2018.

[20]

Li J N, Zhang S L, Wang J D, Gao W, Tian Q. LVreID:person re-identification with long sequence videos. arXiv preprint, arXiv:1712.07286, 2017.

[21]

Felzenszwalb P F, Girshick R B, McAllester D, Ramanan D. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9):1627-1645 doi: 10.1109/TPAMI.2009.167

[22]

Ren S Q, He K M, Girshick R, Sun J. Faster R-CNN:towards real-time object detection with region proposal networks. In:Proceedings of the 2015 Advances in Neural Information Processing Systems (NIPS). Montreal, Quebec, Canada:MIT Press, 2015. 91-99

[23]

Dehghan A, Modiri Assari S, Shah M. GMMCP tracker:globally optimal generalized maximum multi clique problem for multiple object tracking. In:Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA:IEEE, 2015. 4091-4099

[24]

Karanam S, Gou M R, Wu Z Y, Rates-Borras A, Camps O, Radke R J. A Systematic evaluation and benchmark for person re-identification:features, metrics, and datasets, arXiv preprint arXiv:1605.09653, 2016.

[25]

Geng M Y, Wang Y W, Xiang T, Tian Y H. Deep transfer learning for person re-identification. arXiv preprint, arXiv:1611.05244, 2016.

[26]

Lin Y T, Zheng L, Zheng Z D, Wu Y, Yang Y. Improving person re-identification by attribute and identity learning. arXiv preprint arXiv:1703.07220, 2017.

[27]

Matsukawa T, Suzuki E. Person re-identification using CNN features learned from combination of attributes. In:Proceedings of the 23rd International Conference on Pattern Recognition. Cancun, Mexico:IEEE, 2016. 2428-2433

[28]

Zhang Y, Xiang T, Hospedales T M, Lu H C. Deep mutual learning. arXiv:1706.00384, 2017.

[29]

Zheng L, Zhang H H, Sun S Y, Chandraker M, Yang Y, Tian Q. Person re-identification in the wild. In:Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii, USA:IEEE, 2017. 3346-3355

[30]

Zheng Z D, Zheng L, Yang Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In:Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017. 3774-3782

[31]

Zheng Z D, Zheng L, Yang Y. A discriminatively learned CNN embedding for person reidentification. ACM Transactions on Multimedia Computing, Communications, and Applications, 2018, 14(1):Article No. 13

[32]

Shi H L, Yang Y, Zhu X Y, Liao S C, Lei Z, Zheng W S, et al. Embedding deep metric for person re-identification:a study against large variations. In:Proceedings of the 2016 European Conference on Computer Vision. Cham:Springer, 2016. 732-748

[33]

Varior R R, Haloi M, Wang G. Gated Siamese convolutional neural network architecture for human re-identification. In:Proceedings of the 2016 European Conference on Computer Vision. Cham:Springer, 2016. 791-808

[34]

Varior R R, Shuai B, Lu J W, Xu D, Wang G. A Siamese long short-term memory architecture for human re-identification. In:Proceedings of the 2016 European Conference on Computer Vision. Cham:Springer, 2016. 135-153

[35]

Wang Y C, Chen Z Z, Wu F, Wang G. Person re-identification with cascaded pairwise convolutions. In:Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, Utah, USA:IEEE, 2018. 1470-1478

[36]

Cheng D, Gong Y H, Zhou S P, Wang J J, Zheng N N. Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA:IEEE, 2016. 1335-1344

[37]

Hermans A, Beyer L, Leibe B. In defense of the triplet loss for person re-identification. arXiv preprint, arXiv:1703.07737, 2017.

[38]

Liu H, Feng J S, Qi M B, Jiang J G, Yan S C. End-to-end comparative attention networks for person re-identification. IEEE Transactions on Image Processing, 2017, 26(7):3492-3506 doi: 10.1109/TIP.2017.2700762

[39]

Ristani E, Tomasi C. Features for multi-target multi-camera tracking and re-identification. arXiv:1803.10859, 2018.

[40]

Chen W H, Chen X T, Zhang J G, Huang K Q. Beyond triplet loss:a deep quadruplet network for person re-identification. In:Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii, USA:IEEE, 2017. 1320-1329

[41]

Xiao Q Q, Luo H, Zhang C. Margin sample mining loss:a deep learning based method for person re-identification. arXiv preprint, arXiv:1710.00478, 2017.

[42]

Xiao Q Q, Cao K L, Chen H N, Peng F Y, Zhang C. Cross domain knowledge transfer for person re-identification. arXiv preprint, arXiv:1611.06026, 2016.

[43]

Zhang X, Luo H, Fan X, Xiang W L, Sun Y X, Xiao Q Q, et al. AlignedReID:surpassing human-level performance in person re-identification. arXiv preprint, arXiv:1711.08184, 2017.

[44]

Wei L, Zhang S, Yao H, Gao W, Tian Q. GLAD:global-local-alignment descriptor for scalable person re-identification. IEEE Transactions on Multimedia, 2018, 21(4):986-999 http://d.old.wanfangdata.com.cn/Periodical/dbnydxxb200703019

[45]

Zhao H Y, Tian M Q, Sun S Y, Shao J, Yan J J, Yi S, et al. Spindle net:person re-identification with human body region guided feature decomposition and fusion. In:Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii, USA:IEEE, 2017. 907-915

[46]

Zheng L, Huang Y J, Lu H C, Yang Y. Pose invariant embedding for deep person re-identification. arXiv preprint, arXiv:1701.07732, 2017.

[47]

Dai J, Zhang P P, Lu H C, Wang H Y. Video person re-identification by temporal residual learning. arXiv preprint, arXiv:1802.07918, 2018.

[48]

Li Y J, Zhuo L, Li J F, Zhang J, Liang X, Tian Q. Video-based person re-identification by deep feature guided pooling. In:Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, HI, USA:IEEE, 2017. 1454-1461

[49]

Liu H, Jie Z, Jayashree K, et al. Video-based person re-identification with accumulative motion context. IEEE transactions on circuits and systems for video technology, 2017, 28(10):2788-2802 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fcf0184b7ea04fee35ca78c9e93626fe

[50]

Ma X L, Zhu X T, Gong S G, Xie X D, Hu J M, Lam K M, et al. Person re-identification by unsupervised video matching. Pattern Recognition, 2017, 65:197-210 doi: 10.1016/j.patcog.2016.11.018

[51]

McLaughlin N, Martinez del Rincon J, Miller P. Recurrent convolutional network for video-based person re-identification. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA:IEEE, 2016. 1325-1334

[52]

Wang T Q, Gong S G, Zhu X T, Wang S J. Person re-identification by video ranking. In:Proceedings of the 2014 European Conference on Computer Vision. Cham:Springer, 2014. 688-703

[53]

Wang T Q, Gong S G, Zhu X T, Wang S J. Person re-identification by discriminative selection in video ranking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(12):2501-2514 doi: 10.1109/TPAMI.2016.2522418

[54]

You J J, Wu A C, Li X, Zheng W S. Top-push video-based person re-identification. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA:IEEE, 2016. 1345-1353

[55]

Zhang D, Wu W, Cheng H, et al. Image-to-video person re-identification with temporally memorized similarity learning. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 28(10):2622-2632 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=e87c80639a2e57dd4da4d5479c6049ff

[56]

Zhang W, Ma B P, Liu K, Huang R. Video-based pedestrian re-identification by adaptive spatio-temporal appearance model. IEEE Transactions on Image Processing, 2017, 26(4):2042-2054 doi: 10.1109/TIP.2017.2672440

[57]

Zhao R, Ouyang W L, Wang X G. Person re-identification by saliency learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(2):356-370 doi: 10.1109/TPAMI.2016.2544310

[58]

Wu Y, Lin Y T, Dong X Y, Yan Y, Ouyang W L, Yang Y. Exploit the unknown gradually:one-shot video-based person re-identification by stepwise learning. In:Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah, USA:IEEE, 2018. 5177-5186

[59]

Zhu J Y, Park T, Isola P, Efros A A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In:Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017. 2242-2251

[60]

Yi Z L, Zhang H, Tan P, Gong M L. DualGAN:unsupervised dual learning for image-to-image translation. In:Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017. 2868-2876

[61]

Kim T, Cha M, Kim H, Lee J K, Kim J. Learning to discover cross-domain relations with generative adversarial networks. arXiv:1703.05192, 2017.

[62]

Deng W J, Zheng L, Ye Q X, Kang G L, Yang Y, Jiao J B. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. arXiv:1711.07027, 2018.

[63]

Huang Y, Xu J S, Wu Q, Zheng Z D, Zhang Z X, Zhang J. Multi-pseudo regularized label for generated data in person re-identification. arXiv preprint, arXiv:1801.06742, 2018.

[64]

Qian X L, Fu Y W, Xiang T, Wang W X, Qiu J, Wu Y, et al. Pose-normalized image generation for person re-identification. arXiv preprint, arXiv:1712.02225, 2018.

[65]

Zhong Z, Zheng L, Zheng Z D, Li S Z, Yang Y. Camera style adaptation for person re-identification. arXiv:1711.10295, 2018.

[66]

Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. InfoGAN:interpretable representation learning by information maximizing generative adversarial nets. arXiv:1606.03657, 2016.

[67]

Zhao L M, Li X, Zhuang Y T, Wang J D. Deeply-learned part-aligned representations for person re-identification. In:Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017. 3239-3248

[68]

Howard A G, Zhu M L, Chen B, Kalenichenko D, Wang W J, Weyand T, et al. MobileNets:efficient convolutional neural networks for mobile vision applications. arXiv preprint, arXiv:1704.04861, 2017.

[69]

He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA:IEEE, 2016. 770-778

[70]

Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In:Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, Massachusetts, USA:IEEE, 2015. 1-9

[71]

Sarfraz M S, Schumann A, Eberle A, Stiefelhagen R. A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. arXiv:1711.10378, 2018.

[72]

Jiao J N, Zheng W S, Wu A C, Zhu X T, Gong S G. Deep low-resolution person re-identification. In:Proceedings of the 2018 Association for the Advancement of Artificial Intelligence. New Orleans:AAAI, 2018.

[73]

He L X, Liang J, Li H Q, Sun Z. Deep spatial feature reconstruction for partial person re-identification:alignment-free approach. arXiv:1801.00881, 2018.

[74]

Fan H H, Zheng L, Yang Y. Unsupervised person re-identification:clustering and fine-tuning. arXiv preprint, arXiv:1705.10444, 2017.

[75]

Zhong Z, Zheng L, Cao D L, Li S Z. Re-ranking person re-identification with k-reciprocal encoding. In:Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii, USA:IEEE, 2017. 3652-3661

[76]

Liu H, Feng J S, Jie Z Q, Jayashree K, Zhao B, Qi M B, et al. Neural person search machines. In:Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017. 493-501