Low Complexity Multi-object Tracking System Dealing with Occlusions

Aziz Dziri, Marc Duranton, Roland Chapuis

2015

Abstract

In this paper, we propose a vision tracking system primarily targeted for systems with low computing resources. It is based on GMPHD filter and can deal with occlusion between objects. The proposed algorithm is supposed to work in a node of camera network where the cost of the computer processing the information is critical. To achieve a low computing complexity, a basic background subtraction algorithm combined with a connected component analysis method are used to detect the objects of interest. GMPHD was improved to detect occlusions between objects and to handle their identities once the occlusion ends. The occlusion is detected using a low complexity distance criterion that takes into consideration the object’s bounding box. When an occlusion is noticed, the features of the overlapped objects are saved. At the end of the overlapping, the extracted features are compared to the current features of the objects to perform the object reidentification. In our experiments two different features are tested: color histogram features and motion features. The experiments are performed on two datasets: PETS2009 and CAVIAR. The obtained results show that our approach ensures a high improvement of GMPHD filter and has a low computing complexity.

References

  1. Blackman, S. (2004). Multiple hypothesis tracking for multiple target tracking. Aerospace and Electronic Systems Magazine, IEEE, 19(1):5-18.
  2. Blackman, S. and Popoli, R. (1999). Design and Analysis of Modern Tracking Systems. Artech House, Norwood, MA.
  3. caviar (2004). http://homepages.inf.ed.ac.uk/rbf/caviardata1.
  4. Clark, D., Panta, K., and Vo, B.-N. (2006). The gm-phd filter multiple target tracker. In Information Fusion, 2006 9th International Conference on, pages 1-8.
  5. Dziri, A., Duranton, M., and Chapuis, R. (2014). Low complexity multi-target tracking for embedded systems. In Information Fusion (FUSION), 2014 17th International Conference on, pages 1-8.
  6. Edman, V., Maria, A., Granström, K., and Gustafsson, F. (2013). Pedestrian group tracking using the gm-phd filter. In Proceedings of the 21st European Signal Processing Conference :.
  7. Eiselein, V., Arp, D., Patzold, M., and Sikora, T. (2012). Real-time multi-human tracking using a probability hypothesis density filter and multiple detectors. In Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on, pages 325-330.
  8. Geronimo, D., Lopez, A., Sappa, A., and Graf, T. (2010). Survey of pedestrian detection for advanced driver assistance systems. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(7):1239-1258.
  9. Hoseinezhad, R., Vo, B.-N., and Suter, D. (2009). Fast single-view people tracking. In Cognitive Systems with Interactive Sensors (COGIS2009).
  10. Lamard, L., Chapuis, R., and Boyer, J.-P. (2012). Dealing with occlusions with multi targets tracking algorithms for the real road context. In Intelligent Vehicles Symposium (IV), 2012 IEEE, pages 371-376.
  11. Ma, N., Bailey, D., and Johnston, C. (2008). Optimised single pass connected components analysis. In ICECE Technology, 2008. FPT 2008. International Conference on, pages 185-192.
  12. Mahler, R. (2003). Multitarget bayes filtering via first-order multitarget moments. Aerospace and Electronic Systems, IEEE Transactions on, 39(4):1152-1178.
  13. Panta, K., Clark, D., and Vo, B.-N. (2009). Data association and track management for the gaussian mixture probability hypothesis density filter. Aerospace and Electronic Systems, IEEE Transactions on, 45(3):1003- 1016.
  14. http://cs.binghamton.edu/ pets2009 (2009). data/pets2009.html.
  15. Roller, D., Daniilidis, K., and Nagel, H. (1993). Modelbased object tracking in monocular image sequences of road traffic scenes. International Journal of Computer Vision, 10(3):257-281.
  16. Vezzani, R., Baltieri, D., and Cucchiara, R. (2013). People reidentification in surveillance and forensics: A survey. ACM Comput. Surv., 46(2):29:1-29:37.
  17. Vijverberg, J. A., Koeleman, C. J., and With, P. H. N. d. (2009). Tracking rectangular targets in surveillance videos with the gm-phd filter. In Proceedings of the 30-th Symposium on Information Theory in the Benelux, pages 177-184.
  18. Vo, B.-N. and Ma, W.-K. (2006). The gaussian mixture probability hypothesis density filter. Signal Processing, IEEE Transactions on, 54(11):4091-4104.
  19. Wang, L., Hu, W., and Tan, T. (2003). Recent developments in human motion analysis. Pattern Recognition, 36(3):585 - 601.
  20. Wang, Y.-D., Wu, J.-K., Kassim, A., and Huang, W.-M. (2006). Tracking a variable number of human groups in video using probability hypothesis density. In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, volume 3, pages 1127-1130.
  21. Wu, J. and Hu, S. (2010). Phd filter for multi-target visual tracking with trajectory recognition. In Information Fusion (FUSION), 2010 13th Conference on, pages 1-6.
  22. Yilmaz, A., Javed, O., and Shah, M. (2006). Object tracking: A survey. ACM Comput. Surv., 38(4).
Download


Paper Citation


in Harvard Style

Dziri A., Duranton M. and Chapuis R. (2015). Low Complexity Multi-object Tracking System Dealing with Occlusions . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 194-201. DOI: 10.5220/0005316701940201


in Bibtex Style

@conference{visapp15,
author={Aziz Dziri and Marc Duranton and Roland Chapuis},
title={Low Complexity Multi-object Tracking System Dealing with Occlusions},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={194-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005316701940201},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Low Complexity Multi-object Tracking System Dealing with Occlusions
SN - 978-989-758-089-5
AU - Dziri A.
AU - Duranton M.
AU - Chapuis R.
PY - 2015
SP - 194
EP - 201
DO - 10.5220/0005316701940201