Data Association with Graph Network for Multi-Object Tracking

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

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


Multi-Object Tracking (MOT) methods within Tracking-by-Detection paradigm are usually modeled as graph problem. It is challenging to associate objects in dense scenes with frequent occlusion. To further model object interactions and repair detection errors, we use graph network to extract embeddings for data association. Graph neural network makes it possible for embeddings aggregate and update between vertices (detections and trajectories). We both introduce priori confidence to detection attention and trajectory attention, which consider the interaction between occluded objects in the same frame. Based on MHT framework, we train two graph networks for clustering in adjacent frame and association between long spaced tracklets. Experiments on MOT17/20 benchmarks demonstrate the significant improving in tracking accuracy of proposed method and show state-of-the-art performance for MOT with public detections.

Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management - 15th International Conference, KSEM 2022, Proceedings
EditorsGerard Memmi, Baijian Yang, Linghe Kong, Tianwei Zhang, Meikang Qiu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031109829
Publication statusPublished - 2022
Externally publishedYes
Event15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022 - Singapore, Singapore
Duration: 6 Aug 20228 Aug 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13368 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022


  • Data association
  • Graph neural network
  • Multiple object tracking


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