Displacement prediction strategy based on KCF in tracking

Real-time and high precision tracking of moving targets is always a challenging subject in the field of target tracking. In this paper, based on the feature extraction and detection method of kernel correlation filtering algorithm KCF, target displacement prediction is introduced into the algorithm to achieve more accurate tracking effect. In addition, the scale pool and multi-feature (descriptor) strategy of SAMF algorithm are introduced and improved to achieve real-time tracking of moving targets with higher precision. The experimental results show that the proposed algorithm achieves a good tracking effect on moving targets, and has a good performance in tracking accuracy and tracking speed.