Enhancement of multi-target tracking performance via image restoration and face embedding in dynamic environments

Ji Seong Kim, Doo Soo Chang, Yong Suk Choi

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose several methods to improve the performance of multiple object tracking (MOT), especially for humans, in dynamic environments such as robots and autonomous vehicles. The first method is to restore and re-detect unreliable results to improve the detection. The second is to restore noisy regions in the image before the tracking association to improve the identification. To implement the image restoration function used in these two methods, an image inference model based on SRGAN (super-resolution generative adversarial networks) is used. Finally, the third method includes an association method using face features to reduce failures in the tracking association. Three distance measurements are designed so that this method can be applied to various environments. In order to validate the effectiveness of our proposed methods, we select two baseline trackers for comparative experiments and construct a robotic environment that interacts with real people and provides services. Experimental results demonstrate that the proposed methods efficiently overcome dynamic situations and show favorable performance in general situations.

Original languageEnglish
Article number649
Pages (from-to)1-21
Number of pages21
JournalApplied Sciences (Switzerland)
Volume11
Issue number2
DOIs
StatePublished - 2021 Jan 2

Keywords

  • Computer vision
  • Data association
  • Image restoration
  • Multiple object tracking
  • Online object tracking
  • Visual embedding

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