Online Multi-Object Tracking by Learning Discriminative Appearance with Fourier Transform and Partial Least Square Analysis (온라인 다중 객체 추적을 위한 이산 푸리에 변환과 부분 최소 제곱법을 기반한 구별력 있는 외형 학습)

Seong-Ho Lee (이성호); SEUNGHWAN BAE (배승환)

doi:10.9708/jksci.2020.25.02.049

Online Multi-Object Tracking by Learning Discriminative Appearance with Fourier Transform and Partial Least Square Analysis

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(2), pp.49-58
DOI : 10.9708/jksci.2020.25.02.049
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : January 14, 2020
Accepted : February 3, 2020
Published : February 28, 2020

Seong-Ho Lee ¹, SEUNGHWAN BAE ¹

¹인천대학교

Accredited

ABSTRACT

In this study, we solve an online multi-object problem which finds object states (i.e. locations and sizes) while conserving their identifications in online-provided images and detections. We handle this problem based on a tracking-by-detection approach by linking (or associating) detections between frames. For more accurate online association, we propose novel online appearance learning with discrete fourier transform and partial least square analysis (PLS). We first transform each object image into a Fourier image in order to extract meaningful features on a frequency domain. We then learn PLS subspaces which can discriminate frequency features of different objects. In addition, we incorporate the proposed appearance learning into the recent confidence-based association method, and extensively compare our methods with the state-of-the-art methods on MOT benchmark challenge datasets.

KEYWORDS

Vision-based tracking, multi-object tracking, appearance learning, image fourier transform, data association, surveillance system, recognition.

Citation status

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

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

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Online Multi-Object Tracking by Learning Discriminative Appearance with Fourier Transform and Partial Least Square Analysis

ABSTRACT

KEYWORDS

Citation status

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

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Online Multi-Object Tracking by Learning Discriminative Appearance with Fourier Transform and Partial Least Square Analysis

ABSTRACT

KEYWORDS

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