Multi-detector Fusion-based Depth Correlation Filtering Video Multi-target Tracking Algorithm

Abstract

In the detection and tracking task,the detector has mis-detected and missed targets.For video multi-target tracking algorithms that rely on detection information,there will be a large number of false tracking targets and missed targets.Such missed and false targets will last for dozens of frames,resulting in reduced tracking accuracy.Due to this reason,a multi-detector fusion deep correlation filter video multi-target tracking algorithm is proposed.It uses the information of multiple detectors and proposes a new fusion mechanism to reduce the number of missed detections and false detections caused by a single detector,and break the performance limitations of a single detector,which makes the acquisition of new targets more reliable.On the other hand,the deep correlation filter algorithm ECO is used to track the targets one by one,and a series of improvements are proposed on the basis of the original algorithm ECO,which is more suitable for the video multi-target tracking task,and reduces the number of missed targets and identity tag jumps.Finally,experiments are carried out on the MOT17 data set,compared with the traditional video multi-target tracking method IOU17,MOTA of the proposed algorithm improves from 47.6 to 50.3.It is proved that this method has made great improvement in the research of multi-target tracking.

Keywords

• multi-target tracking|multi-detector fusion|deep correlation filter|detection and tracking