Anti-occlusion and Scale Adaptive Target Tracking Algorithm Based on Kernel Correlation Filter

Abstract

The Kernel Correlation Filter Tracking Algorithm(KCF)is a lightweight tracking algorithm with the advantages of fast tracking speed and good effect. However, when the target is occluded and the scale changes, the algorithm will have tracking drift and tracking loss. Aiming at the kernel-related filter tracking algorithm that cannot solve the tracking failure caused by occlusion, target scale changes and other factors in the tracking process, an anti-occlusion and scale-adaptive kernel-related filtering algorithm is proposed. We build a scale pool and use the scale pool to train a one-dimensional fast scale filter to solve the problem of target scale changes. This paper uses the average occlusion distance metric and the size of the context occlusion factor to determine the occlusion state of the target, and dynamically select the learning update rate of the target model according to the target occlusion state. When it is judged that the target is severely occluded, the target position is predicted according to the previous motion state of the target, and the small-range re-detection positioning mechanism proposed in this paper is used to re-detect the target within a certain range of the predicted position. At the same time, the re-detected target is occluded again Judgment to determine whether the target is out of the occlusion. If it is determined that the target is still severely occluded, it means that the target is not out of the occlusion area, the re-detection of the target position is inaccurate, and the predicted position is output. Experimental results show that the accuracy and success rate of the algorithm in this paper are 0.819 and 0.669, which are 8.33% and 7.04% higher than the KCF algorithm. The tracking effect of this algorithm is better than that of KCF algorithm.