  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.
