Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Feature-based detection and correction of occlusions and split of video objects

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This paper proposes a novel algorithm for the real-time detection and correction of occlusion and split in object tracking for surveillance applications. The paper assumes a feature-based model for tracking and is based on the identification of sudden variations of spatio-temporal features of objects to detect occlusions and splits. The detection is followed by a validation stage that uses past tracking information to prevent false detection of occlusion or split. Special care is taken in case of heavy occlusion, when there is a large superposition of objects. For the detection of splits, in addition to the analysis of spatio-temporal changes in objects’ features, our algorithm analyzes the temporal behavior of split objects to discriminate between errors in segmentation and real separation of objects, such as in a deposit event. Both objective and subjective experimental results show the ability of the proposed algorithm to detect and correct, both, split and occlusion of objects. The proposed algorithm is suitable in video surveillance applications due to its good performance in multiple, heavy, and total occlusions, its ability to differentiate between real object separation and faulty object split, its handling of simultaneous occlusion and split events, and its low computational complexity. The algorithm was integrated into an on-line video surveillance system and tested under several conditions with promising results.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Amer, A.: Voting-based simultaneous tracking of multiple video objects. IEEE Trans. Circuits Syst. Video Technol. 15, 1448–1462 (2005)

    Article  Google Scholar 

  2. Amer, A., Regazzoni, C.: Introduction to the special issue on video object processing for surveillance applications. Real Time Imaging. 11, 167–171 (2005)

    Article  Google Scholar 

  3. Bullock, D., Zelek, J.: Real-time tracking for visual interface applications in cluttered and occluding situations. Image Vis. Comput. 22, 1083–1097 (2004)

    Article  Google Scholar 

  4. Cavallaro, A., Steiger, O., Ebrahimi, T.: Tracking video objects in cluttered background. IEEE Trans. Circuits Syst. Video Technol. 15, 575–584 (2005)

    Article  Google Scholar 

  5. Erdem, C., Sankur, B., Tekalp, A.: Performance measures for video object segmentation and tracking. IEEE Trans. Image Process. 13(7), 937–951 (2004)

    Article  Google Scholar 

  6. Isard, M., Blake, A.: CONDENSATION—conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

    Article  Google Scholar 

  7. Mezaris, V., Kompatsiaris, I., Strintzis, M.: Video object segmentation using bayes-based temporal tracking and trajectory-based region merging. IEEE Trans. Circuits Syst. Video Technol. 14, 782–795 (2004)

    Article  Google Scholar 

  8. Mihaylova, L., Brasnett, P., Canagarajah, N., Bull, D.: Object tracking by particle filtering techniques in video sequences. In: Lefebvre, E. (ed.) Advances and Challenges in Multisensor Data and Information Processing, NATO Security Through Science Series: Information and Communication Security, vol. 8, pp. 260–268. IOS Press, Amsterdam (2007)

  9. Nguyen, H.T., Smeulders, A.W.M.: Fast occluded object tracking by robust appearance filter. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1099–1104 (2004)

    Article  Google Scholar 

  10. Nummiaro, K., Koller-Meier, E., Van Gool, L.: Object tracking with an adaptive color-based particle filter. In: Symposium for Pattern Recognition of the DAGM, pp. 355–360 (2002)

  11. Ostheimer, D., Lemay, S., Mayisela, D., Dagba, P., Ghazal, M., Amer, A.: A modular distributed video surveillance system over IP. In: IEEE Canadian Conf. on Electrical and Comp. Engineering, pp. 1001–1004. Ottawa (2006)

  12. Peterfreund, N.: Robust tracking of position and velocity with Kalman snakes. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 564–569 (1998)

    Google Scholar 

  13. Piva, S., Marchesotti, L., Regazzoni, C.: A dynamic model integrating colour and shape information for object tracking in conditions of occlusion. In: Proc. IEEE Int. Conf. Multimedia and Expo, vol. 3, pp. 1547–1550 (2003)

  14. Rittscher, J., Krahnstoever, N., Galup, L.: Multi-target tracking using hybrid particle filtering. In: Seventh IEEE Workshops on Application of Computer Vision WACV/MOTION’05, vol. 1, pp. 447–454 (2005)

  15. Wu, Y., Yu, T.: A field model for human detection and tracking. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 753–765 (2006)

    Article  Google Scholar 

  16. Yang, T., Li, S., Pan, Q., Li, J.: Real-time multiple objects tracking with occlusion handling in dynamic scenes. In: Proc. IEEE Conf. Computer Vision Pattern Recognition, vol. 1, pp. 20–25 (2005)

Download references

Author information

Authors and Affiliations

  1. Advanced Video Systems, Communications Research Centre, Ottawa, ON, Canada

    Carlos Vázquez

  2. Electrical and Computer Engineering, Concordia University, Montréal, QC, Canada

    Mohammed Ghazal & Aishy Amer

Authors
  1. Carlos Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed Ghazal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aishy Amer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos Vázquez.

Additional information

This work was partially supported by the National Science and Engineering Research Council of Canada (NSERC).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vázquez, C., Ghazal, M. & Amer, A. Feature-based detection and correction of occlusions and split of video objects. SIViP 3, 13–25 (2009). https://doi.org/10.1007/s11760-008-0055-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-008-0055-6

Keywords

Search

Navigation