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THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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CenterTrack-EKF: Improved Multi Object Tracking with Extended Kalman Filter

CenterTrack-EKF: 확장된 칼만 필터를 이용한 개선된 다중 객체 추적

  • 양현성 (순천대학교 It-bio융합시스템전공 ) ;
  • 심춘보 (순천대학교 It-bio융합시스템전공 ) ;
  • 정세훈 (순천대학교 컴퓨터공학과)
  • Received : 2024.04.11
  • Accepted : 2024.05.07
  • Published : 2024.05.31
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Abstract

Multi-Object trajectory modeling is a major challenge in MOT. CenterTrack tried to solve this problem with a Heatmap-based method that tracks the object center position. However, it showed limited performance when tracking objects with complex movements and nonlinearities. Considering the degradation factor of CenterTrack as the dynamic movement of pedestrians, we integrated the EKF into CenterTrack. To demonstrate the superiority of our proposed method, we applied the existing KF and UKF to CenterTrack and compared and evaluated it on various datasets. The experimental results confirmed that when EKF was integrated into CenterTrack, it achieved 73.7% MOTA, making it the most suitable filter for CenterTrack.

객체 궤적 모델링은 다중 객체 추적(Multi Object Tracking, MOT)의 주요 과제다. CenterTrack은 객체 중심 위치를 추적하는 Heatmap 기반의 방법으로 이를 해결하고자 했다. 하지만 복잡한 움직임과 비선형성을 가진 객체를 추적할 때 제한적인 성능을 보였다. 우리는 CenterTrack의 성능 저하 요인을 보행자의 동적 움직임으로 간주하여 확장된 칼만 필터(Extended Kalman Filter, EKF)를 CenterTrack에 통합했다. 우리가 제안하는 방법의 우수성을 입증하기 위해 기존 칼만 필터(Kalman Filter, KF)와 무향 칼만 필터(Unscented Kalman Filter, UKF)를 CenterTrack에 적용 후 다양한 데이터셋에 비교 평가했다. 실험결과, EKF를 CenterTrack에 통합했을 때 73.7% MOTA(Multiple Object Tracking Accuracy)를 달성하며 CenterTrack에 가장 적합한 필터임을 확인했다.

Keywords

Acknowledgement

이 논문은 2023년 순천대학교 학술연구비(과제번호: 2023-0293) 공모과제로 연구되었음

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