Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

A Multi-view Approach to Object Tracking in a Cluttered Scene Using Memory

  • Conference paper
Advances in Intelligent Computing (ICIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3645))

Included in the following conference series:

Abstract

In this paper, we propose a new multi-view approach to object tracking method that adapts itself to suddenly changing appearance. The proposed method is based on color-based particle filtering. A short-term memory and a global appearance memory are introduced to handle sudden appearance changes and occlusions of the object of interest in multi-camera environments. A new target model update method is implemented for multiple camera views. Our method is robust and versatile for a modest computational cost. Desirable tracking results are obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Comaniciu, D., Berton, F., Ramesh, V.: Adaptive Resolution System for Distributed Surveillance. Real-Time Imaging 8, 427–437 (2002)

    Article  MATH  Google Scholar 

  2. Kahn, S., Javed, O., Shah, M.: Tracking in Uncalibrated Cameras with Overlapping Field of View. PETS (2001)

    Google Scholar 

  3. Trivedi, M., Mikic, I., Bhonsle, S.: Active Camera Networks and Semantic Event Databased for Intelligent Environments. In: Proc. IEEE Workshop on Human Modelling, Analysis and Synthesis (2000)

    Google Scholar 

  4. Nummiaro, K., Koller-Meier, E., Svoboda, T.: Color-Based Object Tracking in Multi- Camera Environments. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 591–599. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. Kang, H., Cho, S.: Short-term Memory-based Object Tracking. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 597–605. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proc. CVPR 2001 (2001)

    Google Scholar 

  7. Nummiaro, K., Koller-Meier, E., Van Gool, L.: A Color-Based Particle Filter. In: First International Workshop on Generative-Model-Based Vision, in Conjunction with ECCV 2002, pp. 53–60 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Engineering, Catholic University of Korea, #43-1 Yokkok 2-dong Wonmi-Gu, Puchon City Kyonggi-Do, Korea

    Hang-Bong Kang & Sang-Hyun Cho

Authors
  1. Hang-Bong Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sang-Hyun Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. School of Computer & Information Technology, Beijing Jiaotong University, 100044, Beijing, P.R. China

    Xiao-Ping Zhang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

    Guang-Bin Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kang, HB., Cho, SH. (2005). A Multi-view Approach to Object Tracking in a Cluttered Scene Using Memory. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_90

Download citation

  • DOI: https://doi.org/10.1007/11538356_90

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28227-3

  • Online ISBN: 978-3-540-31907-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation