Robust local effective matching model for multi-target tracking

Hao Sheng, Li Hao, Jiahui Chen, Yang Zhang, Wei Ke

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)


Occlusion is one of the main challenges in multi-target tracking, which causes fragments in tracking. In order to handle with fragments, various motion models were proposed. However, motion model has limited effect on dealing with long-term fragments, because the predictability of target motion declines with increase in fragment length. Thus we propose a robust local effective matching model for partial detections to reduce fragment length first. The proposed model is integrated into a network flow based hierarchical framework to solve long-term fragments step-by-step. Initial tracklets are generated for later analysis in the first level. The robust local effective matching model is used in the second level to reduce fragment length. A motion model is utilized in the third level to solve fragments between tracklets. The benchmark results on 2D MOT 2015 dataset were compared with several state-of-the-art trackers and our method got competitive results with those trackers.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
EditorsBing Zeng, Hongliang Li, Qingming Huang, Abdulmotaleb El Saddik, Shuqiang Jiang, Xiaopeng Fan
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319773827
Publication statusPublished - 2018
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 28 Sept 201729 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10736 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th Pacific-Rim Conference on Multimedia, PCM 2017


  • Local effective matching model
  • Long-term fragment
  • Multi-target tracking
  • Network flow
  • Partial detection


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