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Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning

Authors:

Thomas S. HuangAuthors Info & Claims

International Journal of Computer Vision, Volume 58, Issue 1

Pages 55 - 71

https://doi.org/10.1023/B:VISI.0000016147.97880.cd

Published: 01 June 2004 Publication History

Abstract

Visual tracking can be treated as a parameter estimation problem that infers target states based on image observations from video sequences. A richer target representation may incur better chances of successful tracking in cluttered and dynamic environments, and thus enhance the robustness. Richer representations can be constructed by either specifying a detailed model of a single cue or combining a set of rough models of multiple cues. Both approaches increase the dimensionality of the state space, which results in a dramatic increase of computation. To investigate the integration of rough models from multiple cues and to explore computationally efficient algorithms, this paper formulates the problem of multiple cue integration and tracking in a probabilistic framework based on a factorized graphical model. Structured variational analysis of such a graphical model factorizes different modalities and suggests a co-inference process among these modalities. Based on the importance sampling technique, a sequential Monte Carlo algorithm is proposed to provide an efficient simulation and approximation of the co-inferencing of multiple cues. This algorithm runs in real-time at around 30 Hz. Our extensive experiments show that the proposed algorithm performs robustly in a large variety of tracking scenarios. The approach presented in this paper has the potential to solve other problems including sensor fusion problems.

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Cited By

Mondal A(2021)Occluded object tracking using object-background prototypes and particle filterApplied Intelligence10.1007/s10489-020-02047-x51:8(5259-5279)Online publication date: 7-Jan-2021
https://dl.acm.org/doi/10.1007/s10489-020-02047-x
Mondal A(2019)Neuro-probabilistic model for object trackingPattern Analysis & Applications10.1007/s10044-019-00791-622:4(1609-1628)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s10044-019-00791-6
(2018)Multiple object tracking by employing shaped-based features and Kalman filterInternational Journal of Business Intelligence and Data Mining10.5555/3192182.319220213:1-3(331-346)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3192182.3192202
Show More Cited By

Index Terms

Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing

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Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 58, Issue 1

Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology

June 2004

77 pages

ISSN:0920-5691

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Copyright © Copyright © 2004 Kluwer Academic Publishers.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2004

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Cited By

Mondal A(2021)Occluded object tracking using object-background prototypes and particle filterApplied Intelligence10.1007/s10489-020-02047-x51:8(5259-5279)Online publication date: 7-Jan-2021
https://dl.acm.org/doi/10.1007/s10489-020-02047-x
Mondal A(2019)Neuro-probabilistic model for object trackingPattern Analysis & Applications10.1007/s10044-019-00791-622:4(1609-1628)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s10044-019-00791-6
(2018)Multiple object tracking by employing shaped-based features and Kalman filterInternational Journal of Business Intelligence and Data Mining10.5555/3192182.319220213:1-3(331-346)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3192182.3192202
Truong MKim S(2017)Parallel implementation of color-based particle filter for object tracking in embedded systemsHuman-centric Computing and Information Sciences10.1186/s13673-016-0082-17:1(1-13)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1186/s13673-016-0082-1
Abdeslam WTabii YEl Kadiri KLazaar MTabii YChrayah MAl Achhab M(2017)Adaptive Appearance Model in Particle filter based Visual TrackingProceedings of the 2nd international Conference on Big Data, Cloud and Applications10.1145/3090354.3090441(1-5)Online publication date: 29-Mar-2017
https://dl.acm.org/doi/10.1145/3090354.3090441
Xia GLudwig S(2017)Object tracking using Particle Swarm Optimization and Earth mover's distance2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969313(193-200)Online publication date: 5-Jun-2017
https://dl.acm.org/doi/10.1109/CEC.2017.7969313
Zhang SWang JWang ZGong YLiu Y(2015)Multi-target tracking by learning local-to-global trajectory modelsPattern Recognition10.1016/j.patcog.2014.08.01348:2(580-590)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.patcog.2014.08.013
Chen HTang C(2015)Robust tracking using visual cue integration for mobile mixed imagesJournal of Visual Communication and Image Representation10.1016/j.jvcir.2015.04.00630:C(208-218)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1016/j.jvcir.2015.04.006
Wu YJia NSun J(2015)Real-time multi-scale tracking based on compressive sensingThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-014-0942-531:4(471-484)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1007/s00371-014-0942-5
Wang JChen DZhang JLi SWang X(2014)Object tracking with adaptive multicue incremental visual trackerAdvances in Multimedia10.1155/2014/3438602014(9-9)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1155/2014/343860
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