RESOLVING DATA-ASSOCIATION UNCERTAINTY - In Mutli-object Tracking through Qualitative Modules

Saira Saleem Pathan, Ayoub Al-Hamadi, Gerald Krell, Bernd Michaelis

2010

Abstract

In real-time tracking, a crucial challenge is to efficiently build association among the objects. However, real-time interferences~(e.g. occlusion) manifest errors in data association. In this paper, the uncertainties in data association are handled when discrete information is incomplete during occlusion through qualitative reasoning modules. The formulation of the qualitative modules are based on exploiting human-tracking abilities (i.e. common sense) which are integrated with data association technique. Each detected object is described as a node in space with a unique identity and status tag whereas association weights are computed using CWHI and Bhattacharyya coefficient. These weights are input to qualitative modules which interpret the appropriate status of the objects satisfying the fundamental constraints of object's continuity during tracking. The results are linked with Kalman Filter to estimate the trajectories of objects. The proposed approach has shown promising results illustrating its contribution when tested on a set of videos representing various challenges.

References

  1. Bar-Shalom, Y. and Fortmann, T. E. (1987). Tracking and data association, volume 179 of Mathematics in Science and Engineering.
  2. Bennett, B. (2008). Combining logic and probability in tracking and scene interpretation. In Cohn, A. G., Hogg, D. C., Möller, R., and Neumann, B., editors, Logic and Probability for Scene Interpretation, number 08091 in Dagstuhl Seminar Proceedings.
  3. Bennett, B., Magee, D., Cohn, A. G., and Hogg, D. (2008). Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity. Image Vision Computer, 26(1):67-81.
  4. Blake, A. (2006). Visual Tracking: A Short Research Roadmap. Springer, New York.
  5. Frintrop, S., Knigs, A., Hoeller, F., and Schulz, D. (2009). Visual person tracking using a cognitive observation model. In Workshop on People Detection and Tracking at the IEEE ICRA.
  6. Halpern, J. Y. (1990). An analysis of first-order logics of probability. Artificial Intelligence, 46(3):311-350.
  7. Kailath, T. (1967). The divergence and bhattacharyya distance measures in signal selection. IEEE Transactions on Communications[legacy, pre - 1988], 15(1):52-60.
  8. Khan, Z. (2005). MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transaction of Pattern Analysis and Machine Intelligence, 27(11):1805-1918.
  9. Pathan, S., Al-Hamadi, A., Elmezain, M., and Michaelis, B. (2009). Feature-supported multi-hypothesis framework for multi-object tracking using kalman filter. In International Conference on Computer Graphics,Visualization and Computer Vision.
  10. Sherrah, J. and Gong, S. (2000). Resolving visual uncertainty and occlusion through probabilistic reasoning. In In Proceedings of the British Machine Vision Conference, pages 252-261.
  11. Welch, G. and Bishop, G. (1995). An introduction to the kalman filter. Technical report, Chapel Hill, NC, USA.
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Paper Citation


in Harvard Style

Saleem Pathan S., Al-Hamadi A., Krell G. and Michaelis B. (2010). RESOLVING DATA-ASSOCIATION UNCERTAINTY - In Mutli-object Tracking through Qualitative Modules . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 461-466. DOI: 10.5220/0002834604610466


in Bibtex Style

@conference{visapp10,
author={Saira Saleem Pathan and Ayoub Al-Hamadi and Gerald Krell and Bernd Michaelis},
title={RESOLVING DATA-ASSOCIATION UNCERTAINTY - In Mutli-object Tracking through Qualitative Modules},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={461-466},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002834604610466},
isbn={978-989-674-028-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - RESOLVING DATA-ASSOCIATION UNCERTAINTY - In Mutli-object Tracking through Qualitative Modules
SN - 978-989-674-028-3
AU - Saleem Pathan S.
AU - Al-Hamadi A.
AU - Krell G.
AU - Michaelis B.
PY - 2010
SP - 461
EP - 466
DO - 10.5220/0002834604610466