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Feature correspondence in a non-overlapping camera network

机译：非重叠相机网络中的功能对应

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摘要

Person re-identification across multiple cameras is difficult due to viewpoint and illumination variations. Most traditional research focuses on developing invariant features that are unaffected by these variations. However, thus far, there has been no feature developed that is completely invariant, and it is possible that a fully invariant feature may not exist. Therefore, we do not seek to develop these ideal features in this paper. We instead propose a framework for learning a gallery of persons who appear in the camera network frequently. The gallery contains appearance models of these persons from each camera and viewpoint. Given the camera identity, viewpoint identity, person identity, the model is decided. Since these appearance models are specific to each camera and viewpoint, the problems of viewpoint variations and illumination variations between cameras are explicitly solved, and re-identification becomes a ranking problem. Experiments demonstrate that our framework provides significant improvement in addressing the re-identification problem.
机译：由于视点和照明的变化，很难在多个摄像机之间重新识别人。大多数传统研究都集中于开发不受这些变化影响的不变特征。但是，到目前为止，还没有开发出完全不变的特征，并且可能不存在完全不变的特征。因此，我们不在本文中寻求开发这些理想功能。相反，我们提出了一个框架，用于学习经常出现在摄像机网络中的人的画廊。画廊包含每个摄像机和各个角度的这些人的外观模型。给定摄像机身份，视点身份，人身份，就可以确定模型。由于这些外观模型是每个摄像机和视点所特有的，因此摄像机之间视点变化和照度变化的问题得到了明确解决，重新识别成为排名问题。实验表明，我们的框架在解决重新标识问题方面提供了重大改进。

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