Abstract
Person Re-identification is a very challenging task due to inter-class ambiguity caused by similar appearances, and large intra-class diversity caused by viewpoints, illuminations, and poses. To address these challenges, in this article, a graph convolution network based model for person re-identification is proposed to learn more discriminative feature embeddings, where a graph-structured relationship between person images and person parts are together integrated. Graph convolution networks extract common characteristics of the same person, while pyramid feature embedding exploits parts relations and learns stable representation with each person image. We achieve a very competitive performance respectively on three widely used datasets, indicating that the proposed approach significantly outperforms the baseline methods and achieves the state-of-the-art performance.
- Ejaz Ahmed, Michael Jones, and Tim K. Marks. 2015. An improved deep learning architecture for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3908--3916.Google Scholar
- Song Bai, Xiang Bai, and Qi Tian. 2017. Scalable person re-identification on supervised smoothed manifold. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2530--2539.Google Scholar
Cross Ref
- Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann Lecun. 2014. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations.Google Scholar
- Xiaobin Chang, Timothy M. Hospedales, and Tao Xiang. 2018. Multi-level factorisation net for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2109--2118.Google Scholar
Cross Ref
- Weihua Chen, Xiaotang Chen, Jianguo Zhang, and Kaiqi Huang. 2017. Beyond triplet loss: A deep quadruplet network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 403--412.Google Scholar
Cross Ref
- Yanbei Chen, Xiatian Zhu, and Shaogang Gong. 2017. Person re-identification by deep learning multi-scale representations. In Proceedings of the IEEE International Conference on Computer Vision. 2590--2600.Google Scholar
Cross Ref
- De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, and Nanning Zheng. 2016. 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. 1335--1344.Google Scholar
Cross Ref
- Dahjung Chung, Khalid Tahboub, and Edward J. Delp. 2017. A two stream siamese convolutional neural network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1983--1991.Google Scholar
- Michael Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3844--3852.Google Scholar
- Michela Farenzena, Loris Bazzani, Alessandro Perina, Vittorio Murino, and Marco Cristani. 2010. Person re-identification by symmetry-driven accumulation of local features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2360--2367.Google Scholar
Cross Ref
- Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, and Deva Ramanan. 2010. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 9 (2010), 1627--1645.Google Scholar
Digital Library
- Douglas Gray and Hai Tao. 2008. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In Proceedings of the European Conference on Computer Vision. 262--275.Google Scholar
Digital Library
- Zhenyu He, Xin Li, Xinge You, Dacheng Tao, and Yuan Yan Tang. 2016. Connected component model for multi-object tracking. IEEE Transactions on Image Processing 25, 8 (2016), 3698--3711.Google Scholar
Digital Library
- Zhenyu He, Shuangyan Yi, Yiu-Ming Cheung, Xinge You, and Yuan Yan Tang. 2017. Robust object tracking via key patch sparse representation. IEEE Transactions on Cybernetics 47, 2 (2017), 354--364.Google Scholar
- Alexander Hermans, Lucas Beyer, and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. Arxiv Preprint Arxiv:1703.07737 (2017).Google Scholar
- Svebor Karaman, Giuseppe Lisanti, Andrew D. Bagdanov, and Alberto Del Bimbo. 2014. Leveraging local neighborhood topology for large scale person re-identification. Pattern Recognition 47, 12 (2014), 3767--3778.Google Scholar
Cross Ref
- Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. In Proceedings of International Conference on Learning Representations.Google Scholar
- Martin Koestinger, Martin Hirzer, Paul Wohlhart, Peter M. Roth, and Horst Bischof. 2012. Large scale metric learning from equivalence constraints. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2288--2295.Google Scholar
Cross Ref
- Igor Kviatkovsky, Amit Adam, and Ehud Rivlin. 2013. Color invariants for person reidentification. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 7 (2013), 1622--1634.Google Scholar
Digital Library
- Wei Li and Xiaogang Wang. 2013. Locally aligned feature transforms across views. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3594--3601.Google Scholar
Digital Library
- Wei Li, Yang Wu, and Jianqing Li. 2017. Re-identification by neighborhood structure metric learning. Pattern Recognition 61 (2017), 327--338.Google Scholar
Digital Library
- Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. 2014. Deepreid: Deep filter pairing neural network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
Digital Library
- Wei Li, Xiatian Zhu, and Shaogang Gong. 2017. Person re-identification by deep joint learning of multi-loss classification. In Proceedings of the International Joint Conferences on Artificial Intelligence.Google Scholar
Cross Ref
- Wei Li, Xiatian Zhu, and Shaogang Gong. 2018. Harmonious attention network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2285--2294.Google Scholar
Cross Ref
- Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li. 2015. Person re-identification by local maximal occurrence representation and metric learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2197--2206.Google Scholar
- Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2117--2125.Google Scholar
Cross Ref
- Chunxiao Liu, Shaogang Gong, and Chen Change Loy. 2014. On-the-fly feature importance mining for person re-identification. Pattern Recognition 47, 4 (2014), 1602--1615.Google Scholar
Digital Library
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 2605 (2008), 2579--2605.Google Scholar
- Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, and Yoichi Sato. 2016. Hierarchical Gaussian descriptor for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1363--1372.Google Scholar
Cross Ref
- Sateesh Pedagadi, James Orwell, Sergio Velastin, and Boghos Boghossian. 2013. Local Fisher discriminant analysis for pedestrian re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3318--3325.Google Scholar
Digital Library
- Xuelin Qian, Yanwei Fu, Yu-Gang Jiang, Tao Xiang, and Xiangyang Xue. 2017. Multi-scale deep learning architectures for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision. 5399--5408.Google Scholar
Cross Ref
- Peter M. Roth, Martin Hirzer, Martin Koestinger, Csaba Beleznai, and Horst Bischof. 2014. Mahalanobis distance learning for person re-identification. In Person Re-Identification. 247--267.Google Scholar
- M. Saquib Sarfraz, Arne Schumann, Andreas Eberle, and Rainer Stiefelhagen. 2018. A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 420--429.Google Scholar
Cross Ref
- Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009. The graph neural network model. IEEE Transactions on Neural Networks 20, 1 (2009), 61.Google Scholar
Digital Library
- Yantao Shen, Hongsheng Li, Shuai Yi, Dapeng Chen, and Xiaogang Wang. 2018. Person re-identification with deep similarity-guided graph neural network. In Proceedings of the European Conference on Computer Vision (ECCV). 486--504.Google Scholar
Cross Ref
- Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese. 2016. Deep metric learning via lifted structured feature embedding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4004--4012.Google Scholar
Cross Ref
- Chi Su, Jianing Li, Shiliang Zhang, Junliang Xing, Wen Gao, and Qi Tian. 2017. Pose-driven deep convolutional model for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision. 3960--3969.Google Scholar
Cross Ref
- Chi Su, Shiliang Zhang, Junliang Xing, Wen Gao, and Qi Tian. 2016. Deep attributes driven multi-camera person re-identification. In Proceedings of the European Conference on Computer Vision. 475--491.Google Scholar
Cross Ref
- Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. 2018. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In Proceedings of the European Conference on Computer Vision. 480--496.Google Scholar
Cross Ref
- Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations.Google Scholar
- Faqiang Wang, Wangmeng Zuo, Liang Lin, David Zhang, and Lei Zhang. 2016. Joint learning of single-image and cross-image representations for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1288--1296.Google Scholar
Cross Ref
- Longhui Wei, Shiliang Zhang, Hantao Yao, Wen Gao, and Qi Tian. 2017. GLAD: Global-local-alignment descriptor for pedestrian retrieval. In Proceedings of the ACM on Multimedia Conference. 420--428.Google Scholar
Digital Library
- Kilian Q. Weinberger and Lawrence K. Saul. 2009. Distance metric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research 10 (2009), 207--244.Google Scholar
Digital Library
- Tong Xiao, Hongsheng Li, Wanli Ouyang, and Xiaogang Wang. 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. 1249--1258.Google Scholar
Cross Ref
- Yang Yang, Jimei Yang, Junjie Yan, Shengcai Liao, Dong Yi, and Stan Z. Li. 2014. Salient color names for person re-identification. In Proceedings of the European Conference on Computer Vision. 536--551.Google Scholar
- Li Zhang, Tao Xiang, and Shaogang Gong. 2016. Learning a discriminative null space for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1239--1248.Google Scholar
Cross Ref
- Shengping Zhang, Xiangyuan Lan, Yuankai Qi, and Pong C. Yuen. 2017. Robust visual tracking via basis matching. IEEE Transactions on Circuits and Systems for Video Technology 27, 3 (2017), 421--430.Google Scholar
Digital Library
- Shengping Zhang, Xiangyuan Lan, Hongxun Yao, Huiyu Zhou, Dacheng Tao, and Xuelong Li. 2017. A biologically inspired appearance model for robust visual tracking. IEEE Transactions on Neural Networks and Learning Systems 28, 10 (2017), 2357--2370.Google Scholar
Cross Ref
- Shengping Zhang, Yuankai Qi, Feng Jiang, Xiangyuan Lan, Pong C. Yuen, and Huiyu Zhou. 2018. Point-to-set distance metric learning on deep representations for visual tracking. IEEE Transactions on Intelligent Transportation Systems 19, 1 (2018), 187--198.Google Scholar
Cross Ref
- Shengping Zhang, Huiyu Zhou, Feng Jiang, and Xuelong Li. 2015. Robust visual tracking using structurally random projection and weighted least squares. IEEE Transactions on Circuits and Systems for Video Technology 25, 11 (2015), 1749--1760.Google Scholar
Digital Library
- Haiyu Zhao, Maoqing Tian, Shuyang Sun, Jing Shao, Junjie Yan, Shuai Yi, Xiaogang Wang, and Xiaoou Tang. 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. 1077--1085.Google Scholar
Cross Ref
- 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 Scholar
Digital Library
- Wei-Shi Zheng, Shaogang Gong, and Tao Xiang. 2013. Reidentification by relative distance comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 3 (2013), 653--668.Google Scholar
Digital Library
- Zhedong Zheng, Liang Zheng, and Yi Yang. 2017. A discriminatively learned CNN embedding for person reidentification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 1 (2017), 13:1--13:20.Google Scholar
- Zhedong Zheng, Liang Zheng, and Yi Yang. 2017. SVDNet for pedestrian retrieval. In Proceedings of the IEEE International Conference on Computer Vision. 3800--3808.Google Scholar
- Zhedong Zheng, Liang Zheng, and Yi Yang. 2017. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In Proceedings of the IEEE International Conference on Computer Vision. 3754--3762.Google Scholar
Cross Ref
- Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li. 2017. Re-ranking person re-identification with k-reciprocal encoding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1318--1327.Google Scholar
Cross Ref
Index Terms
Spatial Preserved Graph Convolution Networks for Person Re-identification
Recommendations
Robust person re-identification via graph convolution networks
AbstractPerson re-identification (re-id) aims to identity the same person over multiple cameras; it has been successfully applied to various computer vision applications as a fundamental method. Owing to the development of deep learning, person re-id ...
Person re-identification in TV series using robust face recognition and user feedback
In this paper, we present a system for person re-identification in TV series. In the context of video retrieval, person re-identification refers to the task where a user clicks on a person in a video frame and the system then finds other occurrences of ...
A Unified Generative Adversarial Framework for Image Generation and Person Re-identification
MM '18: Proceedings of the 26th ACM international conference on MultimediaPerson re-identification (re-id) aims to match a certain person across multiple non-overlapping cameras. It is a challenging task because the same person's appearance can be very different across camera views due to the presence of large pose ...






Comments