Beyond triplet loss: a deep quadruplet network for person re-identification

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	Beyond triplet loss: a deep quadruplet network for person re-identification
	Weihua Chen1 ; Xiaotang Chen1 ; Jianguo Zhang 2; Kaiqi Huang1
	2017
会议日期	2017-7-21
会议地点	Hawaii, USA
英文摘要	Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/19644]
专题	自动化研究所_智能感知与计算研究中心
作者单位	1.中国科学院自动化研究所 2.英国邓迪大学
推荐引用方式 GB/T 7714	Weihua Chen,Xiaotang Chen,Jianguo Zhang,et al. Beyond triplet loss: a deep quadruplet network for person re-identification[C]. 见:. Hawaii, USA. 2017-7-21.