Coherent Selection of Independent Trackers for Real-time Object Tracking

Salma Moujtahid, Stefan Duffner, Atilla Baskurt

Abstract

This paper presents a new method for combining several independent and heterogeneous tracking algorithms for the task of online single-object tracking. The proposed algorithm runs several trackers in parallel, where each of them relies on a different set of complementary low-level features. Only one tracker is selected at a given frame, and the choice is based on a spatio-temporal coherence criterion and normalised confidence estimates. The key idea is that the individual trackers are kept completely independent, which reduces the risk of drift in situations where for example a tracker with an inaccurate or inappropriate appearance model negatively impacts the performance of the others. Moreover, the proposed approach is able to switch between different tracking methods when the scene conditions or the object appearance rapidly change. We experimentally show with a set of Online Adaboost-based trackers that this formulation of multiple trackers improves the tracking results in comparison to more classical combinations of trackers. And we further improve the overall performance and computational efficiency by introducing a selective update step in the tracking framework.

References

  1. Avidan, S. (2007). Ensemble tracking. IEEE Trans. on PAMI, 29(2):261-271.
  2. Badrinarayanan, V., Perez, P., Le Clerc, F., and Oisel, L. (2007). Probabilistic color and adaptive multi-feature tracking with dynamically switched priority between cues. In Proc. of ICCV.
  3. Bailer, C., Pagani, A., and Stricker, D. (2014). A superior tracking approach: Building a strong tracker through fusion. In Proc. of ECCV, pages 170-185.
  4. Collins, R. T. and Liu, Y. (2005). On-line selection of discriminative tracking features. IEEE Trans. on PAMI, 27(10):1631-1643.
  5. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proc. of CVPR.
  6. Duffner, S., Odobez, J.-M., and Ricci, E. (2009). Dynamic partitioned sampling for tracking with discriminative features. In Proc. of BMVC, London, UK.
  7. Freund, Y. and Schapire, R. (1997). A Decision-theoretic Generalization of On-line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1):119-139.
  8. Grabner, H. and Bischof, H. (2006). On-line boosting and vision. In Proc. of CVPR, pages 260-267.
  9. Grabner, H., Grabner, M., and Bischof, H. (2006). Realtime tracking via on-line boosting. In Proc. of BMVC, pages 47-56.
  10. Hua, C., Wu, H., Chen, Q., Wada, T., and City, W. (2006). A pixel-wise object tracking algorithm with target and background sample. In Proc. of ICPR.
  11. Kittler, J., Hatef, M., Duin, R. P. W., and Matas, J. (1998). On combining classifiers. IEEE Trans. on PAMI, 20(3):226-239.
  12. Kristan, M., Cehovin, L., Pflugfelder, R., Nebehay, G., Fernandez, G., Matas, J., and et al. (2013). The Visual Object Tracking VOT2013 challenge results. In Proc. of ICCV (Workshops).
  13. Kwon, J. and Lee, K. (2010). Visual tracking decomposition. In Proc. of CVPR, pages 1269-1276.
  14. Kwon, J. and Lee, K. (2011). Tracking by sampling trackers. In Proc. of ICCV.
  15. Leichter, I., Lindenbaum, M., and Rivlin, E. (2006). A general framework for combining visual trackers - “black boxes” approach. IJCV, 67(3):343-363.
  16. Maggio, E., Smeraldi, F., and Cavallaro, A. (2007). Adaptive multifeature tracking in a particle filtering framework. IEEE on Circuits and Systems for Video Technology, 17(10):1348-1359.
  17. Moreno-Noguer, F., Sanfeliu, A., and Dimitris, S. (2008). Dependent multiple cue integration for robust tracking. IEEE Trans. on PAMI, 30(4):670-685.
  18. Nickel, K. and Stiefelhagen, R. (2008). Dynamic integration of generalized cues for person tracking. In Proc. of ECCV, pages 514-526.
  19. Perez, P., Vermaak, J., and Blake, A. (2004). Data fusion for visual tracking with particles. Proc. of IEEE, 93(3):495-513.
  20. Smeulder, W. M. A., Dung, M. C., Cucchiara, R., Calderara, S., Deghghan, A., and Shah, M. (2014). Visual tracking: an experimental survey. IEEE Trans. on PAMI.
  21. Stalder, S., Grabner, H., and Gool, L. V. (2009). Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition. In ICCV (WS on On-line Comp. Vis.)), pages 1409-1416.
  22. Stenger, B., Woodley, T., and Cipolla, R. (2009). Learning to track with multiple observers. In Proc. of CVPR.
  23. Triesch, J. and v. d. Malsburg, C. (2001). Democratic integration: Self-organized integration of adaptive cues. Neural Computation, 13(9):2049-2074.
  24. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proc. of CVPR, volume 1.
  25. Yilmaz, A., Li, X., and Shah, M. (2004). Object contour tracking using level sets. In Proc. of ACCV.
  26. Yin, Z., Porikli, F., and Collins, R. T. (2008). Likelihood map fusion for visual object tracking. In IEEE Workshop on Applications of Computer Vision, pages 1-7.
Download


Paper Citation


in Harvard Style

Moujtahid S., Duffner S. and Baskurt A. (2015). Coherent Selection of Independent Trackers for Real-time Object Tracking . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 584-592. DOI: 10.5220/0005311305840592


in Bibtex Style

@conference{visapp15,
author={Salma Moujtahid and Stefan Duffner and Atilla Baskurt},
title={Coherent Selection of Independent Trackers for Real-time Object Tracking},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={584-592},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005311305840592},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - Coherent Selection of Independent Trackers for Real-time Object Tracking
SN - 978-989-758-091-8
AU - Moujtahid S.
AU - Duffner S.
AU - Baskurt A.
PY - 2015
SP - 584
EP - 592
DO - 10.5220/0005311305840592