Consistent-Aware Deep Learning for Person Re-identification in a Camera Network

Ji Lin, Liangliang Ren, Jiwen Lu, Jianjiang Feng, Jie Zhou
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引用次数: 119

Abstract

In this paper, we propose a consistent-aware deep learning (CADL) framework for person re-identification in a camera network. Unlike most existing person re-identification methods which identify whether two body images are from the same person, our approach aims to obtain the maximal correct matches for the whole camera network. Different from recently proposed camera network based re-identification methods which only consider the consistent information in the matching stage to obtain a global optimal association, we exploit such consistent-aware information under a deep learning framework where both feature representation and image matching are automatically learned with certain consistent constraints. Specifically, we reach the global optimal solution and balance the performance between different cameras by optimizing the similarity and association iteratively. Experimental results show that our method obtains significant performance improvement and outperforms the state-of-the-art methods by large margins.
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摄像机网络中一致性感知深度学习的人物再识别
在本文中,我们提出了一个一致性感知深度学习(CADL)框架,用于摄像机网络中的人员再识别。与大多数现有的识别人体图像是否来自同一个人的方法不同,我们的方法旨在获得整个摄像机网络的最大正确匹配。与最近提出的基于相机网络的再识别方法仅考虑匹配阶段的一致性信息以获得全局最优关联不同,我们在深度学习框架下利用这种一致性感知信息,在一定的一致性约束下自动学习特征表示和图像匹配。具体而言,我们通过迭代优化相似度和关联度来达到全局最优解并平衡不同相机之间的性能。实验结果表明,我们的方法获得了显著的性能改进,并且大大优于目前最先进的方法。
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