Embedding Deep Metric for Person Re-identification: A Study Against Large Variations

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• 关键词：Person re ; identification ; Deep learning ; CNN
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9905
• 期：1
• 页码：732-748
• 全文大小：4,467 KB
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• 作者单位：Hailin Shi (17) (18)
  Yang Yang (17) (18)
  Xiangyu Zhu (17) (18)
  Shengcai Liao (17) (18)
  Zhen Lei (17) (18)
  Weishi Zheng (19)
  Stan Z. Li (17) (18)
  
  17. National Laboratory of Pattern Recognition and Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  18. University of Chinese Academy of Sciences, Beijing, China
  19. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
  
• 丛书名：Computer Vision ¨C ECCV 2016
• ISBN：978-3-319-46448-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9905

文摘

Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)’s capability of feature extraction. However, the distribution is unknown, so it is difficult to use the geodesic distance when comparing two samples. In practice, the current deep embedding methods use the Euclidean distance for the training and test. On the other hand, the manifold learning methods suggest to use the Euclidean distance in the local range, combining with the graphical relationship between samples, for approximating the geodesic distance. From this point of view, selecting suitable positive (i.e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations. In this paper, we propose a novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation. In addition, we improve the learning by a metric weight constraint, so that the learned metric has a better generalization ability. Experiments show that these two strategies are effective in learning robust deep metrics for person re-identification, and accordingly our deep model significantly outperforms the state-of-the-art methods on several benchmarks of person re-identification. Therefore, the study presented in this paper may be useful in inspiring new designs of deep models for person re-identification.