Performance Analysis of Fuzzy-Weighted Multiple Instance Learning on Thermal Video-Based Visual Tracking

Nur Ibrahim, Arsyad R. Darlis, Benyamin Kusumoputro

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, performance analysis of fuzzy-Weighted Multiple Instance Learning (WMIL) with the fuzzy logic tracker on thermal video-based visual tracking is presented. Thermal cameras have been used recently in some pedestrian areas, cars, and surveillance areas that need to be monitored all day. A thermal camera with advantages over the other visual-based sensors in low-light conditions is utilized in this research. The paper presents an analysis of visual tracking with an experimental method in the low-light outdoor environment. The thermal camera is used to record object movement used as video sequences to analyze the performance of our proposed system that integrate Type-2 Fuzzy Logic System (T2FLS) and WMIL tracker. The WMIL-T2FLS tracker performance is shown in the failure rate and center location error. The results show that the object in the thermal video sequences can be tracked using WMIL-T2FLS tracker in the low-light outdoor environment with a low level of failure rate and center location error. Then, the WMIL-T2FLS tracker can track the object when it occluded with the other similar object quite accurately. This result was compared with the original WMIL and some state-of-the art of tracking algorithm: DSST, ECO, KCF, SRDCF, and BACF. The research results showed that the WMIL-T2FLS system significantly improved compared with the WMIL method only, with a success rate improvement of at least 35 % and precision of at least 0.2 in 15 m dan 10 m. WMIL-T2FLS tracker also outperform some state-of-the art method and showed good performance in visual tracking at low-light environments.

Original languageEnglish
Pages (from-to)88-94
Number of pages7
JournalJournal of Image and Graphics(United Kingdom)
Volume10
Issue number2
DOIs
Publication statusPublished - Jun 2022

Keywords

  • low-light outdoor environment
  • thermal camera
  • type-2 fuzzy logic system
  • visual tracking
  • weighted multiple instance learning

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