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); Seung-Hwan 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, Seung-Hwan 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.

[confproc] D. Xie / 2004 / A multi-object tracking system for surveillance video analysis / Proceedings of the 17th International Conference on Pattern Recognition 4 : 767~770

[confproc] Y. Ye / 2016 / Object detection and tracking using multi-layer laser for autonomous urban driving / IEEE International Conference on Intelligent Transportation Systems : 259~264

[confproc] K. Fragkiadaki / 2011 / Detection free tracking : Exploiting motion and topology for segmenting and tracking under entanglement / IEEE Conference on Computer Vision and Pattern Recognition : 2073~2080

[journal] Seung-Hwan Bae / 2018 / Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking / IEEE Transactions on Pattern Analysis and Machine Intelligence / Institute of Electrical and Electronics Engineers (IEEE) 40(3) : 595~610

[confproc] K. He / 2017 / Mask R-CNN / Proceedings of the IEEE international conference on computer vision : 2961~2969

[confproc] D. Bolya / 2019 / YOLACT: Real-time Instance Segmentation / Proceedings of the IEEE international conference on computer vision : 9157~9166

[confproc] C. Kim / 2015 / Multiple hypothesis tracking revisited / Proceedings of the IEEE international conference on computer vision : 4696~4704

[confproc] P. Bergmann / 2019 / Tracking without bells and whistles / Proceedings of the IEEE international conference on computer vision : 941~951

[confproc] X. Li / 2010 / A multiple object tracking method using Kalman filter / The 2010 IEEE International Conference on Information and Automation : 1862~1866

[confproc] J. Yoon / 2015 / Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects / The 2015 IEEE Winter Conference on Applications of Computer Vision : 33~40

[confproc] B. Benfold / 2011 / Stable multi-target tracking in real-time surveillance video / IEEE Conference on Computer Vision and Pattern Recognition : 3457~3464

[journal] Junfeng Ge / 2009 / Real-Time Pedestrian Detection and Tracking at Nighttime for Driver-Assistance Systems / IEEE Transactions on Intelligent Transportation Systems / Institute of Electrical and Electronics Engineers (IEEE) 10(2) : 283~298

[journal] Weiming Hu / 2012 / Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model / IEEE Transactions on Pattern Analysis and Machine Intelligence / Institute of Electrical and Electronics Engineers (IEEE) 34(12) : 2420~2440

[journal] Seong-Ho Lee / 2018 / Learning Discriminative Appearance Models for Online Multi-Object Tracking With Appearance Discriminability Measures / IEEE Access / Institute of Electrical and Electronics Engineers (IEEE) 6 : 67316~67328

[confproc] J. Son / 2017 / Multi-object Tracking with Quadruplet Convolutional Neural Networks / IEEE Conference on Computer Vision and Pattern Recognition : 3786~3795

[journal] Joao F. Henriques / 2015 / High-Speed Tracking with Kernelized Correlation Filters / IEEE Transactions on Pattern Analysis and Machine Intelligence / Institute of Electrical and Electronics Engineers (IEEE) 37(3) : 583~596

[journal] Zhangping He / 2018 / Fast Fourier Transform Networks for Object Tracking Based on Correlation Filter / IEEE Access / Institute of Electrical and Electronics Engineers (IEEE) 6 : 6594~6601

[journal] François Bourgeois / 1971 / An extension of the Munkres algorithm for the assignment problem to rectangular matrices / Communications of the ACM / Association for Computing Machinery (ACM) 14(12) : 802~804

[book] H. Wold / 1975 / Quantitative sociology / Academic Press : 307~357

[other] A. Milan / 2016 / MOT16: A benchmark for multi-object tracking / CoRR. arXiv:1603.00831

[journal] P F Felzenszwalb / 2010 / Object Detection with Discriminatively Trained Part-Based Models / IEEE Transactions on Pattern Analysis and Machine Intelligence / Institute of Electrical and Electronics Engineers (IEEE) 32(9) : 1627~1645

[confproc] N. L. Baisa / 2019 / Online multi-object visual tracking using a GM-PHD filter with deep appearance learning / International Conference on Information Fusion

[confproc] R. Sanchez-Matilla / 2016 / Online multi-target tracking with strong and weak detections / European Conference on Computer Vision : 84~99

[confproc] Y. Ban / 2016 / Tracking Multiple Persons Based on a Variational Bayesian Model / European Conference on Computer Vision : 52~67

[confproc] A. Boragule / 2017 / Joint cost minimization for multi-object tracking / IEEE Conference on Advanced Video and Signal Based Surveillance : 1~6

[confproc] Y. Song / 2016 / Online multiple object tracking with the hierarchically adopted gm-phd filter using motion and appearance / IEEE Conference on Consumer Electronics-Asia : 1~4

[confproc] J. Son / 2017 / Multi-object tracking with quadruplet convolutional neural networks / 2017 IEEE Conference on Computer Vision and Pattern Recognition : 3786~3795

[confproc] A. Dehghan / 2015 / GMMCP tracker : Globally optimal generalized maximum multi clique problem for multiple object tracking / IEEE Conference on Computer Vision and Pattern Recognition : 4091~4099

[confproc] H. Pirsiavash / 2011 / Globally-optimal greedy algorithms for tracking a variable number of objects / IEEE Conference on Computer Vision and Pattern Recognition : 1201~1208

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.