skip to main content
10.1145/2499788.2499802acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicimcsConference Proceedingsconference-collections
research-article

On-line object tracking with semi-supervised boosting based particle filter

Published: 17 August 2013 Publication History

Abstract

There are two key issues for the particle filtering based object tracking: the proposed distribution p(Xit|Xiiit-I) and the likelihood p(zt|Xit) between the prediction and the actual observation. The kernel color histogram based particle filter (CHPF) has achieved very good tracking performance with respect to partial occlusion, rotation and scale variations. However, it would easily lose an object when the object has similar appearance as the background or when the illumination changes. To address these problems, in this paper we introduce the Semi-Supervised On-line Boosting algorithm (SSOB) and connect the confidence of SSOB with the observation model of particle filter. This new method, Semi-Supervised Boosting On-line Object Tracking based Particle Filter (SBPF), can better distinguish objects from the background. It also has faster while more robust adaptation to the change of objects' appearance and the environment illumination condition. The core to the enhanced characteristics is implementing the likelihood as the Semi-Supervised On-line Boosting operator. Extensive experiments show the superior performance of this novel method under aforementioned difficult scenarios.

References

[1]
A. Yilmaz, O. Javed, and M. Shah. 2006. Object tracking: A survey. ACM Computing Surveys. 38(4), 1--45.
[2]
K. Nummiaro, E. Koller-Meier, L. Van Gool. 2003. An adaptive color-based particle filter. Image and Vision Computing. 21(1), 99--110.
[3]
R. T. Collins, Y. Liu, and M. Leordeanu. 2005. Online selection of discriminative tracking features. IEEE Trans. PAMI. 27(10), 1631--1643.
[4]
S. Avidan. Ensemble tracking. 2007. IEEE Trans. PAMI. 29(2), 261--271.
[5]
Grabner H, Bischof H. 2006. On-line boosting and vision. In Proc. CVPR, 260--267.
[6]
Grabner H, Leistner C, Bischof H. 2008. Semi-supervised on-line boosting for robust tracking. In Proc. ECCV, 234--247.
[7]
P. Pérez, C. Hue, J. Vermaak, and M. Gangnet. 2002. Color-based probabilistic tracking. In Proc. ECCV.
[8]
VIVID Tracking Evaluation Web Site at: http://vision.cse.psu.edu/data/vividEval/datasets/datasets.html
[9]
C. J. Needham, R. D. Boyle. 2003. Performance Evaluation Metrics and Statistics for Positional Tracker Evaluation. Proc. ICVS 2003, Graz, Austria, 278--289.
[10]
N. Dalal, B. Triggs. 2005. Histograms of oriented gradients for human detection. In Proc. CVPR 2005, San Diego, American, 886--893.

Index Terms

  1. On-line object tracking with semi-supervised boosting based particle filter

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
      August 2013
      419 pages
      ISBN:9781450322522
      DOI:10.1145/2499788
      • Conference Chair:
      • Tat-Seng Chua,
      • General Chairs:
      • Ke Lu,
      • Tao Mei,
      • Xindong Wu
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      • NSF of China: National Natural Science Foundation of China
      • University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
      • Beijing ACM SIGMM Chapter

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 17 August 2013

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. confidence
      2. motion model
      3. object tracking
      4. particle filter
      5. semi-supervised on-line boosting

      Qualifiers

      • Research-article

      Funding Sources

      Conference

      ICIMCS '13
      Sponsor:
      • NSF of China
      • University of Sciences & Technology, Hefei

      Acceptance Rates

      ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;
      Overall Acceptance Rate 163 of 456 submissions, 36%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 78
        Total Downloads
      • Downloads (Last 12 months)2
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 20 Jan 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media