Chen Shen
Xian-Sheng Hua
ABSTRACT
Person re-identification (re-ID), which aims at spotting a person of interest across multiple camera views, has gained more and more attention in computer vision community. In this paper, we propose a novel deep Siamese architecture based on convolutional neural network (CNN) and multi-level similarity perception. According to the distinct characteristics of diverse feature maps, we effectively apply different similarity constraints to both low-level and high-level feature maps, during training stage. Therefore, our network can efficiently learn discriminative feature representations at different levels, which significantly improves the re-ID performance. Besides, our framework has two additional benefits. Firstly, classification constraints can be easily incorporated into the framework, forming a unified multi-task network with similarity constraints. Secondly, as similarity comparable information has been encoded in the network's learning parameters via back-propagation, pairwise input is not necessary at test time. That means we can extract features of each gallery image and build index in an off-line manner, which is essential for large-scale real-world applications. Experimental results on multiple challenging benchmarks demonstrate that our method achieves splendid performance compared with the current state-of-the-art approaches.
- Ejaz Ahmed, Michael Jones, and Tim K Marks. 2015. An improved deep learning architecture for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3908--3916.Google Scholar
- Léon Bottou. 2012. Stochastic gradient descent tricks. Neural networks: Tricks of the trade. Springer, 421--436.Google Scholar
- Dapeng Chen, Zejian Yuan, Badong Chen, and Nanning Zheng. 2016 b. Similarity learning with spatial constraints for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1268--1277.Google Scholar
- Dapeng Chen, Zejian Yuan, Gang Hua, Nanning Zheng, and Jingdong Wang. 2015 a. Similarity learning on an explicit polynomial kernel feature map for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1565--1573.Google Scholar
- Weihua Chen, Xiaotang Chen, Jianguo Zhang, and Kaiqi Huang. 2016 a. A Multi-task Deep Network for Person Re-identification. arXiv preprint arXiv:1607.05369 (2016).Google Scholar
- Ying-Cong Chen, Wei-Shi Zheng, and Jianhuang Lai. 2015 b. Mirror Representation for Modeling View-Specific Transform in Person Re-Identification. IJCAI. Citeseer, 3402--3408. Google ScholarDigital Library
- Sumit Chopra, Raia Hadsell, and Yann LeCun. 2005. Learning a similarity metric discriminatively, with application to face verification Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. Vol. 1. IEEE, 539--546. Google ScholarDigital Library
- Michela Farenzena, Loris Bazzani, Alessandro Perina, Vittorio Murino, and Marco Cristani. 2010. Person re-identification by symmetry-driven accumulation of local features Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2360--2367.Google Scholar
- Niloofar Gheissari, Thomas B Sebastian, and Richard Hartley. 2006. Person reidentification using spatiotemporal appearance Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. Vol. 2. IEEE, 1528--1535. Google ScholarDigital Library
- Matthieu Guillaumin, Jakob Verbeek, and Cordelia Schmid. 2009. Is that you? Metric learning approaches for face identification Computer Vision, 2009 IEEE 12th international conference on. IEEE, 498--505.Google Scholar
- Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Proceedings of the 32nd International Conference on Machine Learning (ICML-15). 448--456. Google ScholarDigital Library
- Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding Proceedings of the 22nd ACM international conference on Multimedia. ACM, 675--678. Google ScholarDigital Library
- Cijo Jose and Franccois Fleuret. 2016. Scalable metric learning via weighted approximate rank component analysis European Conference on Computer Vision. Springer, 875--890.Google Scholar
- Martin Koestinger, Martin Hirzer, Paul Wohlhart, Peter M Roth, and Horst Bischof. 2012. Large scale metric learning from equivalence constraints Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2288--2295. Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks Advances in neural information processing systems. 1097--1105. Google ScholarDigital Library
- Sheng Li, Ming Shao, and Yun Fu. 2015. Cross-View Projective Dictionary Learning for Person Re-Identification. IJCAI. 2155--2161. Google ScholarDigital Library
- Wei Li, Rui Zhao, and Xiaogang Wang. 2012. Human reidentification with transferred metric learning Asian Conference on Computer Vision. Springer, 31--44. Google ScholarDigital Library
- Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. 2014. Deepreid: Deep filter pairing neural network for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 152--159. Google ScholarDigital Library
- Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z Li. 2015. Person re-identification by local maximal occurrence representation and metric learning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2197--2206.Google Scholar
- Shengcai Liao and Stan Z Li. 2015. Efficient psd constrained asymmetric metric learning for person re-identification Proceedings of the IEEE International Conference on Computer Vision. 3685--3693. Google ScholarDigital Library
- Jiawei Liu, Zheng-Jun Zha, QI Tian, Dong Liu, Ting Yao, Qiang Ling, and Tao Mei. 2016. Multi-Scale Triplet CNN for Person Re-Identification Proceedings of the 2016 ACM on Multimedia Conference. ACM, 192--196. Google ScholarDigital Library
- Xiaokai Liu, Hongyu Wang, Yi Wu, Jimei Yang, and Ming-Hsuan Yang. 2015. An ensemble color model for human re-identification Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on. IEEE, 868--875. Google ScholarDigital Library
- Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, and Yoichi Sato. 2016. Hierarchical gaussian descriptor for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1363--1372.Google Scholar
- Alexis Mignon and Frédéric Jurie. 2012. Pcca: A new approach for distance learning from sparse pairwise constraints Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2666--2672. Google ScholarDigital Library
- Hyeonjoon Moon and P Jonathon Phillips. 2001. Computational and performance aspects of PCA-based face-recognition algorithms. Perception, Vol. 30, 3 (2001), 303--321.Google ScholarCross Ref
- Sakrapee Paisitkriangkrai, Chunhua Shen, and Anton van den Hengel. 2015. Learning to rank in person re-identification with metric ensembles Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1846--1855.Google Scholar
- Sateesh Pedagadi, James Orwell, Sergio Velastin, and Boghos Boghossian. 2013. Local fisher discriminant analysis for pedestrian re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3318--3325. Google ScholarDigital Library
- Bryan Prosser, Wei-Shi Zheng, Shaogang Gong, Tao Xiang, and Q Mary. 2010. Person Re-Identification by Support Vector Ranking. BMVC, Vol. Vol. 2. 6.Google ScholarCross Ref
- Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, and others. 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision Vol. 115, 3 (2015), 211--252. Google ScholarDigital Library
- Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 815--823.Google Scholar
- Hailin Shi, Yang Yang, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Weishi Zheng, and Stan Z Li. 2016. Embedding deep metric for person re-identification: A study against large variations European Conference on Computer Vision. Springer, 732--748.Google Scholar
- Zhiyuan Shi, Timothy M Hospedales, and Tao Xiang. 2015. Transferring a semantic representation for person re-identification and search Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4184--4193.Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research Vol. 15, 1 (2014), 1929--1958. Google ScholarDigital Library
- Arulkumar Subramaniam, Moitreya Chatterjee, and Anurag Mittal. 2016. Deep Neural Networks with Inexact Matching for Person Re-Identification Advances in Neural Information Processing Systems. 2667--2675.Google Scholar
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1--9.Google ScholarCross Ref
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2818--2826.Google Scholar
- Rahul Rama Varior, Mrinal Haloi, and Gang Wang. 2016. Gated siamese convolutional neural network architecture for human re-identification European Conference on Computer Vision. Springer, 791--808.Google Scholar
- Faqiang Wang, Wangmeng Zuo, Liang Lin, David Zhang, and Lei Zhang. 2016 b. Joint learning of single-image and cross-image representations for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1288--1296.Google Scholar
- Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, and Heng Tao Shen. 2016 a. A survey on learning to hash. arXiv preprint arXiv:1606.00185 (2016).Google Scholar
- Kilian Q Weinberger, John Blitzer, and Lawrence Saul. 2006. Distance metric learning for large margin nearest neighbor classification. Advances in neural information processing systems Vol. 18 (2006), 1473.Google Scholar
- Tong Xiao, Hongsheng Li, Wanli Ouyang, and Xiaogang Wang. 2016. Learning deep feature representations with domain guided dropout for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1249--1258.Google Scholar
- Fei Xiong, Mengran Gou, Octavia Camps, and Mario Sznaier. 2014. Person re-identification using kernel-based metric learning methods European conference on computer vision. Springer, 1--16.Google Scholar
- Yang Yang, Shengcai Liao, Zhen Lei, and Stan Z Li. 2016. Large Scale Similarity Learning Using Similar Pairs for Person Verification. AAAI. 3655--3661. Google ScholarDigital Library
- Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. 2014. Deep metric learning for person re-identification. Pattern Recognition (ICPR), 2014 22nd International Conference on. IEEE, 34--39. Google ScholarDigital Library
- Li Zhang, Tao Xiang, and Shaogang Gong. 2016 b. Learning a discriminative null space for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1239--1248.Google Scholar
- Ying Zhang, Baohua Li, Huchuan Lu, Atshushi Irie, and Xiang Ruan. 2016 a. Sample-specific svm learning for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1278--1287.Google Scholar
- Rui Zhao, Wanli Ouyang, and Xiaogang Wang. 2013. Person re-identification by salience matching. In Proceedings of the IEEE International Conference on Computer Vision. 2528--2535. Google ScholarDigital Library
- Rui Zhao, Wanli Ouyang, and Xiaogang Wang. 2014. Learning mid-level filters for person re-identification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 144--151. Google ScholarDigital Library
- Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. 2015. Scalable person re-identification: A benchmark. In Proceedings of the IEEE International Conference on Computer Vision. 1116--1124. Google ScholarDigital Library
- Liang Zheng, Yi Yang, and Alexander G Hauptmann. 2016 a. Person Re-identification: Past, Present and Future. arXiv preprint arXiv:1610.02984 (2016).Google Scholar
- Liang Zheng, Yi Yang, and Qi Tian. 2016 b. SIFT meets CNN: a decade survey of instance retrieval. arXiv preprint arXiv:1608.01807 (2016).Google Scholar
Index Terms
- Deep Siamese Network with Multi-level Similarity Perception for Person Re-identification
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