An Improved CamShift Algorithm for Target Tracking in Video Surveillance
Chunrong Zhang, Yuansong Qiao, Enda Fallon, Chiangqiao Xu · Arrow@dit (Dublin Institute of Technology) · 2021
Target tracking in a cluttered environment remains a challenging research topic. The task of target tracking is a key component of video surveillance and monitoring systems. In this paper, we present an improved CamShift algorithm for tracking a target in video sequences in real time. Firstly, a background-weighted histogram which helps to distinguish the target from the background and other targets is introduced. Secondly, the window size is calculated to track the target as its shape and orientation change. Finally, we use a Kalman Filter to avoid being trapped by a local maximum. The introduction of the Kalman Filter also enables track recovery following a total occlusion. Experiments on various video sequences illustrate the proposed algorithm performs better than the original CamShift approach.