Skip to main content
SpringerLink
Log in
Book cover

Proceedings of the 2012 International Conference on Information Technology and Software Engineering pp 401–407Cite as

A Fast Tracking Algorithm for Multiple-Objects in Occlusions

  • Conference paper
  • First Online:

  • 730 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 212))

Abstract

Occlusion problem is one of difficult points in moving target tracking, which also is one of key techniques to intelligent vision. Based on moving objects detection, minimum enclosing rectangle of human-body top half area is selected as tracing window, which can eliminate the interference of ground shadow, avoid the disturbing of leg movement, and can reduce the amount of computation. Then color histogram of tracing window is calculated by use of the simplified color space, which combines minimum distance matching is used for target matching under non-occlusion; if occlusion occurs, color histogram matching and trajectory prediction is adopted for target matching, with this method, moving object can be tracked correctly under serious occlusion. The experimental results demonstrate that the algorithm is rapid and robust, it can effectively overcome multi-targets tracking in complex scenarios.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Xue C, Zhu M, Liu C-X (2009) Review of tracking algorithms under occlusions. Chinese J Optics Appl Optics 2(5):388–394 (in Chinese)

    Google Scholar 

  2. Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: Proceedings of IEEE conference on computer vision and pattern recognition. IEEE Computer Society, Hilton Head, SC, USA vol 2, pp 142–149

    Google Scholar 

  3. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575

    Article  Google Scholar 

  4. Babu RV, Pérez P, Bouthemy P (2007) Robust tracking with motion estimation and local kernel-based color modeling”. Image Vis Comput 25(8):1205–1216

    Article  Google Scholar 

  5. Shen Z, Yang X, Huang X (2008) Study on target model update method in mean shift algorithm. ACTA Automatica Sinica, 35(5):478–483 (in Chinese)

    Google Scholar 

  6. Collins RT, Liu Y, Leordeanu M (2005) Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell 27(10):1631–1643

    Article  Google Scholar 

  7. Yongzhong W, Yan L, Chunhui Z et al (2008) Kernel-based tracking based on adaptive fusion of multiple cues. ACTA Automatica Sinica 34(4):393–399 (in Chinese)

    MATH  Google Scholar 

  8. Hong L, Ze Yu, Hongbin Z et al (2009) Robust human tracking based on multicue integration and mean-shift. Pattern Recogn Lett 30(9):827–837

    Article  Google Scholar 

  9. Wang ZQ, Fan YF, Zhang GL et al (2007) Robust face tracking algorithm with occlusions. SPIE 6786:X1–X10

    Google Scholar 

  10. Xiaorong LI, J IL Kov VP (2001) A survey of maneuvering target tracking: part II: ballistic target models. In: Proceedings of 2001 SPIE conference on signal and data processing of small targets. The international society for optical engineering, San Diego, pp 559–581

    Google Scholar 

  11. Farina A, Benvenu TID, Ristic B (2002) Tracking aballistic target: comparison of several nonlinear filters. IEEE Trans Aerosp Electron Syst 38(3):854–867

    Article  Google Scholar 

  12. Satoh Y, Okatanit, Deguchik (2004) A color-based tracking by Kalman particle filter. In: Proceedings of the 17th international conference on pattern recognition. IEEE computer society, Cambridge, pp 502–505

    Google Scholar 

  13. Jia JP, Wang Q, Chai YM (2006) Object tracking by multi-degrees of freedom mean-shift procedure combined with the Kalman particle filter algorithm. In: Proceedings of the 2006 international conference on machine learning and cybernatics. Dalian, pp 3793–3797

    Google Scholar 

  14. Zeng W, Zhu G, Chen J, Tang D (2010) Robust tracking algorithm based on multi-feature fusion and particle filter. J Comput App 30(3):643–645 (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Science, Xi’an University of Technology, No. 5, Jinhua South Road, Xi’an Shaanxi, 710048, China

    Zhigang Zhang & Lei Bai

  2. School of Electronics and Information Engineering, Xi’an Jiaotong University, No. 28, Xianning West Road, Xi’an Shaanxi, 710049, China

    Zhigang Zhang

  3. Xi’an University of Technology Engineering Training Center, No. 58, Yanxiang Road, Xi’an Shaanxi, 710054, China

    Junqin Huang

Authors
  1. Zhigang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junqin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhigang Zhang .

Editor information

Editors and Affiliations

  1. Beijing Jiaotong University, Beijing, China, People's Republic

    Wei Lu

  2. Beijing Jiaotong University, Beijing, China, People's Republic

    Guoqiang Cai

  3. Beijing Jiaotong University, Beijing, China, People's Republic

    Weibin Liu

  4. Beijing Jiaotong University, Beijing, China, People's Republic

    Weiwei Xing

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, Z., Bai, L., Huang, J. (2013). A Fast Tracking Algorithm for Multiple-Objects in Occlusions. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. Lecture Notes in Electrical Engineering, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34531-9_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34531-9_42

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34530-2

  • Online ISBN: 978-3-642-34531-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation