Surveillance camera networks are a useful infrastructure for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on target re-identification and trajectory association problems to track the target. However, since camera networks can generate enormous amount of video data, inefficient schemes for making re-identification or trajectory association queries can incur prohibitively large computational requirements. In this paper, we address the problem of intelligent scheduling of re-identification queries in a multi-camera tracking setting. To this end, we formulate the target tracking problem in a camera network as an MDP and learn a reinforcement learning based policy that selects a camera for making a re-identification query. The proposed approach to camera selection does not assume the knowledge of the camera network topology but the resulting policy implicitly learns it. We have also shown that such a policy can be learnt directly from data. Using the NLPR MCT and the Duke MTMC multi-camera multi-target tracking benchmarks, we empirically show that the proposed approach substantially reduces the number of frames queried.

监控摄像机网络是用于各种视觉分析应用程序的有用基础架构，在该应用程序中，可以基于整个网络上的目标跟踪来进行高级推断和预测。大多数多摄像机跟踪工作都着重于目标的重新识别和轨迹关联问题以跟踪目标。但是，由于摄像机网络可以生成大量的视频数据，因此用于进行重新识别或轨迹关联查询的低效方案可能会导致过高的计算需求。在本文中，我们解决了在多摄像机跟踪设置中重新识别查询的智能调度问题。为此，我们将摄像机网络中的目标跟踪问题公式化为MDP，并学习基于强化学习的策略，该策略选择摄像机进行重新识别查询。所提出的摄像机选择方法不假设摄像机网络拓扑知识，但是由此产生的策略会隐式学习它。我们还表明，可以直接从数据中学习这种策略。使用NLPR MCT和Duke MTMC多摄像机多目标跟踪基准，我们从经验上证明了所提出的方法大大减少了查询的帧数。