A Reinforcement Learning Approach to Target Tracking in a Camera Network

TL;DR

This paper introduces a reinforcement learning method for target tracking in camera networks that leverages spatial and temporal information to predict camera transitions, without relying on network topology assumptions.

Contribution

The paper proposes a Q-learning based approach that learns camera transition policies using spatial and temporal cues, independent of camera network topology.

Findings

Effective in learning camera network topology

Improves target tracking accuracy in disjoint camera views

Validated on NLPR MCT dataset

Abstract

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.