Multiple Object Tracking Using Re-Identification Model with Attention Module

Woo Jin Ahn, Koung Suk Ko, Myo Taeg Lim, Dong Sung Pae, Tae Koo Kang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Multi-object tracking (MOT) has gained significant attention in computer vision due to its wide range of applications. Specifically, detection-based trackers have shown high performance in MOT, but they tend to fail in occlusive scenarios such as the moment when objects overlap or separate. In this paper, we propose a triplet-based MOT network that integrates the tracking information and the visual features of the object. Using a triplet-based image feature, the network can differentiate similar-looking objects, reducing the number of identity switches over a long period. Furthermore, an attention-based re-identification model that focuses on the appearance of objects was introduced to extract the feature vectors from the images to effectively associate the objects. The extensive experimental results demonstrated that the proposed method outperforms existing methods on the ID switch metric and improves the detection performance of the tracking system.

Original languageEnglish
Article number4298
JournalApplied Sciences (Switzerland)
Volume13
Issue number7
DOIs
Publication statusPublished - 2023 Apr

Bibliographical note

Funding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (Grant No. 2022R1F1A1073543).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • computer vision
  • deep learning
  • multiple object tracking
  • re-identification

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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