Skip to main content
SpringerLink
Log in

An improvement on an MCMC-based video tracking algorithm

  • Applied Problems
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

This paper presents an approach to fully automatic people tracking in surveillance video recorded by stable camera. We propose an improvement on Benfold et al. tracking-by-detection algorithm [1]. We extend the basic algorithm through filtering of person detector results and the scene entrance/exit positions construction. Moreover, the paper presents a modified method for tracklet position estimation. We compare several tracklet construction algorithms such as “Flock of Features” and normalized cross correlation. Our experiments reveal that all the proposed modifications improve both robustness and precision of tracks compared to the basic algorithm.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. B. Benfold and I. Reid, “Stable multi-target tracking in real-time surveillance video,” in Proc. Computer Vision and Pattern Recognition (Colorado Springs, 2011), pp. 3457–3464.

    Google Scholar 

  2. M. Breitenstein, F. Reichlin, B. Leibe, E. KollerMeier, and L. Van Gool, “Robust tracking-by-detection using a detector confidence particle filter,” Int. Conf. Comput. Vision, No. 12, 1515–1522 (2009).

    Google Scholar 

  3. H. Nguyen and B. Bhanu, “Real-time pedestrian tracking with bacterial foraging optimization,” Adv. Video Signal-Based Surv., No. 9, 37–42 (2012).

    Google Scholar 

  4. W. Choi and S. Savarese, “A unified framework for multi-target tracking and collective activity recognition,” Proc. Europ. Conf. Comput. Vision, No. 12, 215–230 (2012).

    Google Scholar 

  5. S. Oh, S. Russell, and S. Sastry, “Markov chain Monte Carlo data association for general multiple-target tracking problems,” Decision Control 1 (43), 735–742 (2004).

    Google Scholar 

  6. V. Prisacariu and I. Reid, FastHOG–a real-time GPU implementation of HOG. http://www.robots.ox.ac.uk/~lav/Papers/prisacariu_re id_tr2310_09/prisacariu_reid_tr2310_09.pdf

  7. J. Shi and C. Tomasi, “Good features to track,” in Proc. Computer Vision and Pattern Recognition Conf. (Seattle, 1994), pp. 593–600.

    Google Scholar 

  8. C. Tomasi and T. Kanade, Detection and Tracking of Point Features. http://www.ces.clemson.edu/~stb/klt/tomasikanade-techreport-1991.pdf

  9. M. Kolsch and M. Turk, “Fast 2D hand tracking with flocks of features and multi-cue integration,” in Proc. Computer Vision and Pattern Recognition Workshop (Washington, 2004), p. 158.

    Google Scholar 

  10. W. Freeman, D. Anderson, P. Beardsley, C. Dodge, M. Roth, C. Weissman, W. Yerazunus, H. Kage, K. Kyuma, Y. Miyake, and K. Tanaka, “Computer vision for interactive computer graphics,” IEEE Comput. Graph. Appl. 18 (3), 42–53 (1998).

    Article  Google Scholar 

  11. B. Fulkerson, A. Vedaldi, and S. Soatto, “Class segmentation and object localization with superpixel neighborhoods,” in Proc. 12th Int. IEEE Conf. on Computer Vision (Kyoto, 2009), pp. 670–677.

    Google Scholar 

  12. R. E. Kalman, et al., “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82 (1), 35–45 (1960).

    Article  Google Scholar 

  13. K. Bernardin and R. Stiefelhagen, “Evaluation multiple object tracking performance: the CLEAR MOT metrics,” EURASIP J. Image Video Image Video 1. Processing 2008 (3), 1–10 (2008).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graphics and Media Lab, Moscow State University, Moscow, Russia

    E. V. Shalnov & A. S. Konushin

  2. LLC Video Analysis Technologies, ul. Skulptora Mukhinoi 7, Moscow, Russia

    V. S. Konushin

Authors
  1. E. V. Shalnov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. S. Konushin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. S. Konushin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. V. Shalnov.

Additional information

The article was translated by the authors. p ]This paper uses the materials of the report submitted at the 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies,” Samara, Russia, September 23–28, 2013.

Anton S. Konushin was born in 1980. Graduated from the Department of Computational Mathematics and Cybernetics of Moscow State University in 2002. Defended his PhD thesis in 2005 at the Keldysh Institute of Applied Mathematics. Assistant professor at the Department of Computing Systems and Automation and head of the Graphics and Media Lab. The author of 57 papers.

Vadim S. Konushin was born in 1985. Graduated from the Department of Computational Mathematics and Cybernetics of Moscow State University in 2007. Director of LLC Video Analysis Technologies. His scientific interests are computer vision, biometry, and video analytics. The author of 24 papers.

Evgeny V. Shalnov was born in 1991. Graduated from the Department of Computational Mathematics and Cybernetics of Moscow State University in 2013. PhD student at the Department of Computing Systems and Automation. An author of 3 papers. His scientific interests are machine learning, computer vision, probabilistic graphical models, and video analytics. The author of three papers.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shalnov, E.V., Konushin, V.S. & Konushin, A.S. An improvement on an MCMC-based video tracking algorithm. Pattern Recognit. Image Anal. 25, 532–540 (2015). https://doi.org/10.1134/S1054661815030220

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661815030220

Keywords

Search

Navigation