Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 184-190.doi: 10.11896/jsjkx.210600004

• Computer Graphics & Multimedia • Previous Articles     Next Articles

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

SHEN Xiang-pei1, DING Yan-rui1,2   

  1. 1 Laboratory of Media Design and Software Technology,Jiangnan University,Wuxi,Jiangsu 214122,China
    2 Schoolof Science,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:2021-06-01 Revised:2021-10-21 Published:2022-08-02
  • About author:SHEN Xiang-pei,born in 1994,postgraduate.His main research interests include human action recognition and target tracking,etc.
    DING Yan-rui,born in 1976,Ph.D,professor.Her main research interests include intelligent computing and artificial intelligence,etc.
  • Supported by:
    National Natural Science Foundation of China(61772237) and Six Talent Climax Foundation of Jiangsu(XYDXX-030).

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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.

Key words: Deep correlation filter, Detection and tracking, Multi-detector fusion, Multi-target tracking

CLC Number: 

  • TP391
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