research-article

Robust object tracking via multi-cue fusion

Authors:

Guangjun ZhangAuthors Info & Claims

Signal Processing, Volume 139, Issue C

Pages 86 - 95

https://doi.org/10.1016/j.sigpro.2017.04.008

Published: 01 October 2017 Publication History

Abstract

A long-term object tracking method based on calibrated binocular cameras by fusing information of the two channels and binocular geometry constraints is proposed.The stereo filter which is built based on the epipolar geometry of the binocular cameras is effective to filter out false detection proposed by pre-trained object detector on both of the two channels.Experimental results demonstrate that the proposed method can deal with occlusion, scale variation and out-of-view situation well. Object tracking is one of the fundamental problems and an active research area in computer vision during the last decade. Although a wide variety of methods have been proposed, the long-term object tracking is still a challenging problem when dealing with occlusion, out-of-view, scale and illumination variation. To address these challenges, we propose a robust visual object tracking method based on binocular vision in this paper. Our method formulates the object tracking problem in a multi-cue fusion framework which allows our system recover from tracking drift and occlusion. For each channel of the binocular cameras, the coarse object state is estimated by combining the information of motion model, detection and online tracker. Stereo filter is designed to check the object candidate consistency of the two channels. The final object state estimation is determined by fusing the two-channel information and binocular geometry constraints. Experimental results demonstrate the effectiveness of proposed method.

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Cited By

Kumar AWalia GSharma K(2020)A novel approach for multi-cue feature fusion for robust object trackingApplied Intelligence10.1007/s10489-020-01649-950:10(3201-3218)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s10489-020-01649-9
Liang YWang MGuo YZheng W(2020)On large appearance change in visual trackingNeural Computing and Applications10.1007/s00521-019-04094-z32:10(6089-6109)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s00521-019-04094-z
Mahmoud ASherif S(2018)Robust tracking of multiple objects in video by adaptive fusion of subband particle filtersIET Computer Vision10.1049/iet-cvi.2018.537612:8(1207-1218)Online publication date: 30-Aug-2018
https://dl.acm.org/doi/10.1049/iet-cvi.2018.5376

Index Terms

Robust object tracking via multi-cue fusion
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
        Tracking
      2. Computer vision tasks
        Scene understanding
  2. Computer graphics
    1. Animation
      1. Motion capture

Index terms have been assigned to the content through auto-classification.

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cover image Signal Processing

Signal Processing Volume 139, Issue C

October 2017

178 pages

ISSN:0165-1684

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Copyright © Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 October 2017

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Cited By

Kumar AWalia GSharma K(2020)A novel approach for multi-cue feature fusion for robust object trackingApplied Intelligence10.1007/s10489-020-01649-950:10(3201-3218)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s10489-020-01649-9
Liang YWang MGuo YZheng W(2020)On large appearance change in visual trackingNeural Computing and Applications10.1007/s00521-019-04094-z32:10(6089-6109)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s00521-019-04094-z
Mahmoud ASherif S(2018)Robust tracking of multiple objects in video by adaptive fusion of subband particle filtersIET Computer Vision10.1049/iet-cvi.2018.537612:8(1207-1218)Online publication date: 30-Aug-2018
https://dl.acm.org/doi/10.1049/iet-cvi.2018.5376

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