| Summary: | In multi-target tracking tasks combined with detector detection information, missing detections often lead to some targets missed, target identity tag conversion, etc., thereby reducing tracking accuracy. To solve this problem, a multi-target tracking algorithm based on motion information optimization and correlation filter is proposed. After obtaining target detection information, kernelized correlation filter (KCF) is used to track target, and the target's motion information and image information are integrated to handle the problem of missing tracking due to inaccu-rate detection, reducing the fragmented trajectory. At the same time, the smoothing constraint of confidence map is introduced on the basis of KCF to evaluate occlusion degree of targets, which achieves the adaptive update of target template in KCF and deals with the problem of template pollution caused by occlusion. Finally, the experimental results on the MOT Challenge MOT17 data set show that compared with the traditional detection and tracking algor-ithm, high-speed tracking-by-detection without using image information (IOU17), multiple object tracking accuracy (MOTA) of proposed algorithm is improved by 2.43%, and it has better stability and accuracy.
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