Validation of blind region learning and tracking

J. Black, Dimitrios Makris, T. Ellis
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引用次数: 13

Abstract

Multi view tracking systems enable an object's identity to be preserved as it moves through a wide area surveillance network of cameras. One limitation of these systems is an inability to track objects between blind regions, i.e. pans of the scene that are not observable by the network of cameras. Recent interest has been shown in blind region learning and tracking but not much work has been reported on the systematic performance evaluation of these algorithms. The main contribution of this paper is to define a set of novel techniques that can be employed to validate a camera topology model, and a blind region multi view tracking algorithm.
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盲区学习与跟踪的验证
多视图跟踪系统使物体在通过广域摄像机监控网络时能够保持其身份。这些系统的一个限制是无法跟踪盲区之间的物体,即摄像机网络无法观察到的场景。近年来,人们对盲区学习和盲区跟踪产生了兴趣,但对这些算法的系统性能评估的研究还不多。本文的主要贡献是定义了一套新的技术,可用于验证摄像机拓扑模型和盲区多视图跟踪算法。
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