  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.
