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A Self-Supervised Detector Scheduler for Efficient Tracking-by-Detection Mechanism

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(10), pp.19-28
  • DOI : 10.9708/jksci.2022.27.10.019
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 8, 2022
  • Accepted : September 28, 2022
  • Published : October 31, 2022

Dae-Hyeon Park 1 Seong-Ho Lee 1 Seung-Hwan Bae 1

1인하대학교

Accredited

ABSTRACT

In this paper, we propose the Detector Scheduler which determines the best tracking-by-detection (TBD) mechanism to perform real-time high-accurate multi-object tracking (MOT). The Detector Scheduler determines whether to run a detector by measuring the dissimilarity of features between different frames. Furthermore, we propose a self-supervision method to learn the Detector Scheduler with tracking results since it is difficult to generate ground truth (GT) for learning the Detector Scheduler. Our proposed self-supervision method generates pseudo labels on whether to run a detector when the dissimilarity of the object cardinality or appearance between frames increases. To this end, we propose the Detector Scheduling Loss to learn the Detector Scheduler. As a result, our proposed method achieves real-time high-accurate multi-object tracking by boosting the overall tracking speed while keeping the tracking accuracy at most.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.