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首页> 外文会议>2013 Seventh International Conference on Distributed Smart Cameras >Context-aware reinforcement learning for re-identification in a video network
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Context-aware reinforcement learning for re-identification in a video network

机译：用于视频网络中重新识别的上下文感知强化学习

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摘要

Re-identification of people in a large camera network has gained popularity in recent years. The problem still remains challenging due to variations across cameras. A variety of techniques which concentrate on either features or matching have been proposed. Similar to majority of computer vision approaches, these techniques use fixed features and/or parameters. As the operating conditions of a vision system change, its performance deteriorates as fixed features and/or parameters are no longer suited for the new conditions. We propose to use context-aware reinforcement learning to handle this challenge. We capture the changing operating conditions through context and learn mapping between context and feature weights to improve the re-identification accuracy. The results are shown using videos from a camera network that consists of eight cameras.
机译：近年来，大型摄像机网络中人们的重新识别越来越流行。由于摄像机之间的差异，问题仍然具有挑战性。已经提出了集中于特征或匹配的各种技术。与大多数计算机视觉方法类似，这些技术使用固定的功能和/或参数。随着视觉系统的工作条件改变，其性能会下降，因为固定的功能和/或参数不再适合新条件。我们建议使用上下文感知强化学习来应对这一挑战。我们通过上下文捕获变化的操作条件，并学习上下文和特征权重之间的映射，以提高重新识别的准确性。使用来自由八个摄像头组成的摄像头网络中的视频显示结果。

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