Group Guided Data Association for Multiple Object Tracking

Yubin Wu, Hao Sheng, Shuai Wang, Yang Liu, Zhang Xiong, Wei Ke

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

4 Citations (Scopus)


Multiple Object Tracking (MOT) usually adopts the Tracking-by-Detection paradigm, which transforms the problem into data association. However, these methods are restricted by detector performance, especially in dense scenes. In this paper, we propose a novel group-guided data association, which improves the robustness of MOT to error detections and increases tracking accuracy in occlusion areas. The tracklets are firstly clustered into groups of related motion patterns by a graph neural network. Using the idea of grouping, the data association is divided into two stages: intra-group and inter-group. For the intra-group, based on the structural relationship between objects, detections are recovered and associated by min-cost network flow. For inter-group, the tracklets are associated with the proposed hypotheses to solve long-term occlusion and reduce false positives. The experiments on the MOTChallenge benchmark prove our method’s effects, which achieves competitive results over state-of-the-art methods.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2022 - 16th Asian Conference on Computer Vision, Proceedings
EditorsLei Wang, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031262920
Publication statusPublished - 2023
Event16th Asian Conference on Computer Vision, ACCV 2022 - Macao, China
Duration: 4 Dec 20228 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13847 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Asian Conference on Computer Vision, ACCV 2022


  • Data association
  • Multiple object tracking
  • Target grouping


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