Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 370-375.doi: 10.11896/jsjkx.201000115

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Multi-object Tracking Algorithm Based on YOLOv3 and Hierarchical Data Association

LIU Yan1,2, QIN Pin-le1, ZENG Jian-chao1,2   

  1. 1 Shanxi Medical Imaging and Data Analysis Engineering Research Center,North University of China,Taiyuan 030051,China
    2 School of Electrical and Control Engineering,North University of China,Taiyuan 030051,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:LIU Yan,born in 1995,master's degree student,is a member of China ComputerFederation.Her main research interests include multi-target tracking,digitalimage processing,computer vision.
    ZENG Jian-chao,born in 1963,Ph.D,is a member of China Computer Federation.His main research interests include medical image and maintenance decision of complex system.
  • Supported by:
    Shanxi Provincial Key Research and Development Plan(201803D31212-1).

Abstract: In order to alleviate the real-time problem of multi-object tracking methods and the tracking difficulty caused by the high similarity of appearance and the excessive number of error detection in the tracking process,a new multi-object tracking method is proposed,which is based on the improved YOLOv3 and hierarchical data association.As the lightweight network MobileNet uses the deep separable convolution to compress the original network,so as to reduce the network parameters,we uses MobileNet to replace the main structure of YOLOv3 network while retaining the multi-scale prediction part of YOLOv3,so as to reduce the complexity of the network and make the method meet the real-time requirements.Compared with the detection network used in other multi-object tracking methods,we proposed detection network model size is 91 M,and the single detection time can reach 3.12 s.At the same time,the algorithm introduces hierarchical data association method based on object appearance features and motion features.Compared with the method using only appearance features,the hierarchical data association method improves the evaluation index MOTA by 6.5 and MOTP by 1.7.On the MOT16 data set,the tracking accuracy can reach 77.2% and has good anti-jamming ability and real-time performance.

Key words: Deep learning, Hierarchical data association, Lightweight network, Multi-object tracking, YOLOv3

CLC Number: 

  • TP391.41
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