A Reinforcement Learning Approach to Target Tracking in a Camera Network
This addresses surveillance challenges for incremental improvement in handling disjoint views and illumination variation.
The paper tackles target tracking in camera networks by using reinforcement learning to predict the next camera based on spatial and temporal states, achieving performance demonstrated on the NLPR MCT dataset.
Target tracking in a camera network is an important task for surveillance and scene understanding. The task is challenging due to disjoint views and illumination variation in different cameras. In this direction, many graph-based methods were proposed using appearance-based features. However, the appearance information fades with high illumination variation in the different camera FOVs. We, in this paper, use spatial and temporal information as the state of the target to learn a policy that predicts the next camera given the current state. The policy is trained using Q-learning and it does not assume any information about the topology of the camera network. We will show that the policy learns the camera network topology. We demonstrate the performance of the proposed method on the NLPR MCT dataset.