Huangpeng Dai
ABSTRACT
Person re-identification (re-id) has received ever-increasing research focus, because of its important role in video surveillance applications. This paper addresses the re-id problem between visible images of color cameras and infrared images of infrared cameras, which is significant in case that the appearance information is insufficient in poor illumination conditions. In this field, there are two key challenges, i.e., the difficulty to locate the discriminative information to re-identify the same person between visible and infrared images, and the difficulty to learn a robust metric for such large-scale cross-modality retrieval. In this paper, we propose a novel human body parts assistance network (BANet) to tackle the two challenges above. BANet mainly focuses on extracting discriminative information and learning robust features by leveraging the human body part cues. Extensive experiments demonstrate that the proposed approach outperforms the baseline and the state-of-the-art methods.
- Huangpeng Dai, Qing Xie, Yanchun Ma, Yongjian Liu, and Shengwu Xiong. 2020. RGB-Infrared Person Re-identification via Image Modality Conversion. In Proceedings of International Conference on Pattern Recognition (ICPR) . 592--598.Google Scholar
- Pingyang Dai, Rongrong Ji, Haibin Wang, Qiong Wu, and Yuyu Huang. 2018. Cross-modality person re-identification with generative adversarial training. In Proceedings of the 27th International Joint Conference on Artificial Intelligence . 677--683.Google ScholarDigital Library
- Navneet Dalal and Bill Triggs. 2005. Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) , Vol. 1. Ieee, 886--893.Google Scholar
- Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, et almbox. 2018. Recent advances in convolutional neural networks. Pattern Recognition , Vol. 77 (2018), 354--377.Google ScholarDigital Library
- 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
- Martin Koestinger, Martin Hirzer, Paul Wohlhart, Peter M Roth, and Horst Bischof. 2012. Large scale metric learning from equivalence constraints. In 2012 IEEE conference on computer vision and pattern recognition. IEEE, 2288--2295.Google ScholarCross Ref
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems , Vol. 25 (2012), 1097--1105.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. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2197--2206.Google ScholarCross Ref
- Shengcai Liao and Stan Z Li. 2015. Efficient psd constrained asymmetric metric learning for person re-identification. In Proceedings of the IEEE international conference on computer vision . 3685--3693.Google ScholarDigital Library
- Liang Lin, Guangrun Wang, Wangmeng Zuo, Xiangchu Feng, and Lei Zhang. 2016. Cross-domain visual matching via generalized similarity measure and feature learning. IEEE transactions on pattern analysis and machine intelligence , Vol. 39, 6 (2016), 1089--1102.Google Scholar
- Dat Tien Nguyen, Hyung Gil Hong, Ki Wan Kim, and Kang Ryoung Park. 2017. Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors , Vol. 17, 3 (2017), 605.Google ScholarCross Ref
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818--2826.Google ScholarCross Ref
- Huibing Wang, Yang Wang, Zhao Zhang, Xianping Fu, Li Zhuo, Mingliang Xu, and Meng Wang. 2020. Kernelized multiview subspace analysis by self-weighted learning. IEEE Transactions on Multimedia (2020).Google ScholarDigital Library
- Yang Wang. 2021. Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and Fusion. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) , Vol. 17, 1s (2021), 1--25.Google ScholarDigital Library
- Zhixiang Wang, Zheng Wang, Yinqiang Zheng, Yung-Yu Chuang, and Shin'ichi Satoh. 2019. Learning to reduce dual-level discrepancy for infrared-visible person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 618--626.Google ScholarCross Ref
- Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh. 2016. Convolutional pose machines. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 4724--4732.Google ScholarCross Ref
- Ancong Wu, Wei-Shi Zheng, Hong-Xing Yu, Shaogang Gong, and Jianhuang Lai. 2017. RGB-infrared cross-modality person re-identification. In Proceedings of the IEEE international conference on computer vision. 5380--5389.Google ScholarCross Ref
- Lin Wu, Yang Wang, and Ling Shao. 2018. Cycle-consistent deep generative hashing for cross-modal retrieval. IEEE Transactions on Image Processing , Vol. 28, 4 (2018), 1602--1612.Google ScholarDigital Library
- Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, and Ling Shao. 2019. Few-shot deep adversarial learning for video-based person re-identification. IEEE Transactions on Image Processing , Vol. 29 (2019), 1233--1245.Google ScholarCross Ref
- Mang Ye, Xiangyuan Lan, Jiawei Li, and Pong Yuen. 2018a. Hierarchical discriminative learning for visible thermal person re-identification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarCross Ref
- Mang Ye, Chao Liang, Zheng Wang, Qingming Leng, and Jun Chen. 2015. Ranking optimization for person re-identification via similarity and dissimilarity. In Proceedings of the 23rd ACM international conference on Multimedia . 1239--1242.Google ScholarDigital Library
- Mang Ye, Chao Liang, Yi Yu, Zheng Wang, Qingming Leng, Chunxia Xiao, Jun Chen, and Ruimin Hu. 2016. Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing. IEEE Transactions on Multimedia , Vol. 18, 12 (2016), 2553--2566.Google ScholarDigital Library
- Mang Ye, Zheng Wang, Xiangyuan Lan, and Pong C Yuen. 2018b. Visible thermal person re-identification via dual-constrained top-ranking.. In IJCAI , Vol. 1. 2.Google Scholar
- Liang Zheng, Yi Yang, and Alexander G Hauptmann. 2016. Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984 (2016).Google Scholar
- Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017a. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision . 2223--2232.Google ScholarCross Ref
- Xiatian Zhu, Botong Wu, Dongcheng Huang, and Wei-Shi Zheng. 2017b. Fast open-world person re-identification. IEEE Transactions on Image Processing , Vol. 27, 5 (2017), 2286--2300.Google ScholarDigital Library
Index Terms
- Visible-infrared Person Re-identification with Human Body Parts Assistance
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