Enhancing network flow for multi-target tracking with detection group analysis

Chao Li, Kun Qian, Jiahui Chen, Guangtao Xue, Hao Sheng, Wei Ke

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


Multi-target tracking (MTT) has been a research hotspot in the field of computer vision. The objective is forming the trajectory of multiple targets in a given video. However, the useful detection and tracklet relationship during the tracking process are not fully explored in most current algorithms and it leads to the accumulation of errors. We introduce a novel Detection Group, which includes the detections within a temporal and spatial threshold and then model the relationship between Detection Group(DG) and close tracklets. Although the minimum-cost network flow algorithm has been proven to be a successful strategy for multi-target tracking, but it still has one main drawback: due to the fact that useful corresponding detection and tracklet relationships are not well modeled, the network flow based tracker can only model low-level detection relationship without high-level detection set information. To cope with this problem, we extend the classical minimum-cost network flow algorithm within the tracking-by-detection paradigm by incorporating additional constraints. In our experiment, we achieved encouraging result on the MOT17 benchmark and our result is comparable to the current state of the art trackers.

Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management - 11th International Conference, KSEM 2018, Proceedings
EditorsWeiru Liu, Bo Yang, Fausto Giunchiglia
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319993645
Publication statusPublished - 2018
Event11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018 - Changchun, China
Duration: 17 Aug 201819 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11061 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018


  • Detection group
  • Multi-target tracking
  • Network flow


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