Deep Hybrid Similarity Learning for Person Re-identification
Zhu JQ(朱建清); Huanqiang Zeng; Liao SC(廖胜才); Lei Z(雷震); Canhui Cai; Li Xin Zheng
2017-08-01
发表期刊IEEE Transactions on Circuits and Systems for Video Technology
卷号99期号:8页码:1
摘要Person Re-IDentification (Re-ID) aims to match person images captured from two non-overlapping cameras. In this paper, a deep hybrid similarity learning (DHSL) method for person Re-ID based on a convolution neural network (CNN) is proposed. In our approach, a light CNN learning feature pair for the input image pair is simultaneously extracted. Then, both the element-wise absolute difference and multiplication of the CNN learning feature pair are calculated. Finally, a hybrid similarity function is designed to measure the similarity between the feature pair, which is realized by learning a group of weight coefficients to project the element-wise absolute difference and multiplication into a similarity score. Consequently, the proposed DHSL method is able to reasonably assign complexities of feature learning and metric learning in a CNN so that the performance of person Re- ID is improved. Experiments on three challenging person Re-ID databases, QMUL GRID, VIPeR and CUHK03, illustrate that the proposed DHSL method is superior to multiple state-of-the-art person Re-ID methods.
关键词行人再辨识
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20621
专题模式识别国家重点实验室_生物识别与安全技术研究
推荐引用方式
GB/T 7714
Zhu JQ,Huanqiang Zeng,Liao SC,et al. Deep Hybrid Similarity Learning for Person Re-identification[J]. IEEE Transactions on Circuits and Systems for Video Technology,2017,99(8):1.
APA Zhu JQ,Huanqiang Zeng,Liao SC,Lei Z,Canhui Cai,&Li Xin Zheng.(2017).Deep Hybrid Similarity Learning for Person Re-identification.IEEE Transactions on Circuits and Systems for Video Technology,99(8),1.
MLA Zhu JQ,et al."Deep Hybrid Similarity Learning for Person Re-identification".IEEE Transactions on Circuits and Systems for Video Technology 99.8(2017):1.
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