Anti-occlusion tracking algorithm of video target based on prediction and re-matching strategy

Abstract

Accurately locating the video target in the process of occlusion and recurrence will be very important for effective follow-up of the target. For the problem of poor applicability of Mean Shift and its improved algorithm when the target is heavily occluded, this paper proposes an anti-occlusion video target tracking algorithm based on prediction and re-matching strategy. Firstly, dynamically combining the Mean Shift algorithm with the Kalman filter, this paper achieves stable tracking of un-occluded target. Secondly, when the target is occluded, Kalman filter is combined with the target prior information to predict the position of the occluded target. Finally, in the recurrence process of occluded targets, the target is re-matched through the normalized cross-correlation method to obtain target optimal position, and then the target can be quickly and accurately located. The simulation results show that the proposed method has strong anti-occlusion and reliability tracking in the video target tracking process.