论文标题
使用n-Step boottagping的深Q学习，用于在摄像机网络中进行目标跟踪的智能查询
Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping
论文作者
论文摘要
监视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，可以根据整个网络的目标跟踪进行高级推断和预测。大多数多相机跟踪的工作都集中在目标重新识别和轨迹关联问题以跟踪目标。但是，由于相机网络可以生成大量的视频数据，因此用于重新识别或轨迹关联查询的效率低下方案可能会产生较大的计算要求。在本文中，我们解决了在多相机跟踪设置中重新识别查询的智能调度的问题。为此，我们将相机网络中的目标跟踪问题作为MDP提出，并学习基于增强学习的策略，该策略选择了用于进行重新识别查询的相机。提出的相机选择方法并不假设相机网络拓扑的知识，而是由此隐含地学习了摄像机网络拓扑。我们还表明，可以直接从数据中学习这样的策略。使用NLPR MCT和Duke MTMC多与摄像机多目标跟踪基准测试，我们从经验上表明，所提出的方法大大减少了查询的帧数。
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.
