article

Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking

Authors:

Junliang XingAuthors Info & Claims

International Journal of Computer Vision, Volume 127, Issue 8

Pages 1063 - 1083

https://doi.org/10.1007/s11263-018-01147-z

Published: 01 August 2019 Publication History

Abstract

High-order motion information is important in multi-target tracking (MTT) especially when dealing with large inter-target ambiguities. Such high-order information can be naturally modeled as a multi-dimensional assignment (MDA) problem, whose global solution is however intractable in general. In this paper, we propose a novel framework to the problem by reshaping MTT as a rank-1 tensor approximation problem (R1TA). We first show that MDA and R1TA share the same objective function and similar constraints. This discovery opens a door to use high-order tensor analysis for MTT and suggests the exploration of R1TA. In particular, we develop a tensor power iteration algorithm to effectively capture high-order motion information as well as appearance variation. The proposed algorithm is evaluated on a diverse set of datasets including aerial video sequences containing ariel borne dense highway scenes, top-view pedestrian trajectories, multiple similar objects, normal view pedestrians and vehicles. The effectiveness of the proposed algorithm is clearly demonstrated in these experiments.

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Cited By

Chen JXi ZLu JJi J(2022)Multi-object tracking based on network flow model and ORB featureApplied Intelligence10.1007/s10489-021-03042-652:11(12282-12300)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10489-021-03042-6
Yang JGe HYang JTong YSu S(2022)Online multi-object tracking using multi-function integration and tracking simulation trainingApplied Intelligence10.1007/s10489-021-02457-552:2(1268-1288)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10489-021-02457-5
Xiang JXu GMa CHou J(2021)End-to-End Learning Deep CRF Models for Multi-Object Tracking Deep CRF ModelsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297584231:1(275-288)Online publication date: 6-Jan-2021
https://dl.acm.org/doi/10.1109/TCSVT.2020.2975842
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cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 127, Issue 8

August 2019

202 pages

ISSN:0920-5691

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Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2019

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Cited By

Chen JXi ZLu JJi J(2022)Multi-object tracking based on network flow model and ORB featureApplied Intelligence10.1007/s10489-021-03042-652:11(12282-12300)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10489-021-03042-6
Yang JGe HYang JTong YSu S(2022)Online multi-object tracking using multi-function integration and tracking simulation trainingApplied Intelligence10.1007/s10489-021-02457-552:2(1268-1288)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10489-021-02457-5
Xiang JXu GMa CHou J(2021)End-to-End Learning Deep CRF Models for Multi-Object Tracking Deep CRF ModelsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297584231:1(275-288)Online publication date: 6-Jan-2021
https://dl.acm.org/doi/10.1109/TCSVT.2020.2975842
Dendorfer POs̆ep AMilan ASchindler KCremers DReid IRoth SLeal-Taixé L(2021)MOTChallenge: A Benchmark for Single-Camera Multiple Target TrackingInternational Journal of Computer Vision10.1007/s11263-020-01393-0129:4(845-881)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11263-020-01393-0
Yang HGao SWu XZhang Y(2020)Online multi-object tracking using KCF-based single-object tracker with occlusion analysisMultimedia Systems10.1007/s00530-020-00675-426:6(655-669)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s00530-020-00675-4

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