Intelligent Camera Selection Decisions for Target Tracking in a Camera Network

Camera Selection Decisions (CSD) are highly useful for several applications in a multi-camera network. For example, CSD benefit multi-camera target tracking by reducing the number of candidate cameras to look for the target’s next location. The correct candidate cameras, decreases the number of false Re-ID queries as well as the computation time. Also, in multi-camera trajectory forecasting (MCTF) to predict where a person will re-appear in the camera network along with the transition time. These applications require a large amount of annotated data for training. In this paper, we use state-representation learning with a reinforcement learning based policy to effectively and efficiently make camera selection decisions. We further demonstrate that by using learned state representations, as opposed to hand-crafted state variables, we are able to achieve state-of-the-art results on camera selection, while reducing the training time for the RL policy. Along with this, we use a reward function that helps to reduce the amount of supervision in training the policy in a semi-supervised way. We report our results on four datasets: NLPR_MCT, DukeMTMC, CityFlow, and WNMF dataset. We show that an RL policy reduces unnecessary Re-ID queries and therefore the false alarms, scales well to larger camera networks, and is target-agnostic.