Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping
This addresses the computational inefficiency in surveillance camera networks for applications like visual analytics, though it is incremental as it builds on existing tracking methods.
The paper tackles the problem of reducing computational costs in multi-camera target tracking by intelligently scheduling re-identification queries, using a reinforcement learning policy that learns camera selection without topology knowledge, and shows substantial reductions in frames queried on benchmarks like NLPR MCT and Duke MTMC.
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.