Visual tracking of partially observable targets with suboptimal filtering

Author(s): M. Xu ; T. Ellis ; S.J. Godsill ; G.A. Jones
DOI: 10.1049/iet-cvi.2009.0060

For access to this article, please select a purchase option:

Buy article PDF

Buy Knowledge Pack

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership

Recommend Title Publication to library

IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Author(s): M. Xu ¹ ; T. Ellis ² ; S.J. Godsill ³ ; G.A. Jones ²
- Affiliations: 1: Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, People's Republic of China
  2: Digital Imaging Research Centre, Kingston University, Kingston upon Thames, UK
  3: Department of Engineering, University of Cambridge, Cambridge, UK
Source: Volume 5, Issue 1, January 2011, p. 1 – 13
DOI: 10.1049/iet-cvi.2009.0060 , Print ISSN 1751-9632, Online ISSN 1751-9640

A comparative study of four suboptimal tracking algorithms that can cope with missing measurements of low-level visual features during partial occlusion is presented. An approach to identify missing measurements in two-dimensional object tracking is formulated. The comparison starts from a symbolic analysis in Kalman filtering and ends with a performance evaluation in Monte Carlo simulations in which the objects manoeuvre and undergo moderate size variations during occlusion. It is found that the algorithm for estimating unobservables from observables outperforms the others in terms of mean square error, robustness and readiness for implementation.

References

1. 1)
  - Javed, O., Shah, M.: `Tracking and object classification for automated surveillance', Proc. European Conf. on Computer Vision, Copenhagen, 2002, Denmark, p. 343–357.
2. 2)
  - F. Bremond , M. Thonnat . Tracking multiple non-rigid objects in video sequences. IEEE Trans. Circuits Syst. Video Technol. , 5 , 585 - 591
3. 3)
  - http://www.cvg.rdg.ac.uk/datasets/index.html.
4. 4)
  - M. Xu , T. Ellis . Augmented tracking with incomplete observation and probabilistic reasoning. Image Vis. Comput. , 11 , 1202 - 1217
5. 5)
  - C. Motamed . Motion detection and tracking using belief indicators for an automatic visual-surveillance system. Image Vis. Comput. , 11 , 1192 - 1201
6. 6)
  - Xu, M., Ellis, T.: `Partial observation vs. blind tracking through occlusion', Proc. British Machine Vision Conf., 2002, Cardiff, UK, p. 777–786.
7. 7)
  - I. Haritaoglu , D. Hartwood , L.S. Davis . Real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. , 809 - 830
8. 8)
  - V. Leung , A. Colombo , J. Orwell , S.A. Velastin . Modelling periodic scene elements for visual surveillance. IET Comput. Vis. , 2 , 88 - 98
9. 9)
  - Stauffer, C., Grimson, W.E.L.: `Adaptive background mixture models for real-time tracking', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1999, Fort Collins, USA, p. 246–252.
10. 10)
  - M. Valera , S.A. Velastin . Intelligent distributed surveillance system: a review. IEE Proc. Vis. Image Signal Process. , 192 - 204
11. 11)
  - M.S. Grewal , A.P. Andrews . (1993) Kalman filtering: theory and practice.
12. 12)
  - Mammen, J.P., Chaudhuri, S., Agrawal, T.: `Simultaneous tracking of both hands by estimation of erroneous observations', Proc. British Machine Vision Conf., 2001, Manchester, UK, p. 83–92.
13. 13)
  - C. Casella , R.L. Berger . (1990) Statistical inference.
14. 14)
  - Remagnino, P., Baumberg, A., Grove, T.: `An integrated traffic and pedestrian model-based vision system', Proc. British Machine Vision Conf., 1997, Essex, UK, p. 380–389.
15. 15)
  - Koller, D., Weber, J., Malik, J.: `Robust multiple car tracking with occlusion reasoning', Proc. European Conf. on Computer Vision, 1994, Stockholm, Sweden, p. 189–196.
16. 16)
  - Intille, S.S., Davis, J.W., Bobick, A.F.: `Real-time closed-world tracking', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1997, San Juan, Puerto Rico, p. 697–703.
17. 17)
  - Rosales, R., Sclaroff, S.: `Improved tracking of multiple humans with trajectory prediction and occlusion modelling', Proc. IEEE Workshop on Interpretation of Visual Motion, 1998, Santa Barbara, USA, p. 117–123.
18. 18)
  - Ellis, T., Makris, D., Black, J.: `Learning a multi-camera topology', Proc. IEEE Joint Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2003, Nice, France, p. 165–171.
19. 19)
  - Dockstader, S.L., Tekalp, A.M.: `Tracking multiple objects in the presence of articulated and occluded motion', Proc. IEEE Workshop on Human Motion, 2000, Austin, USA, p. 88–95.
20. 20)
  - Y. Bar-Shalom , T.E. Fortmann . (1988) Tracking and data association.
21. 21)
  - A. Amer . Voting-based simultaneous tracking of multiple video objects. IEEE Trans. Circuits Syst. Video Technol. , 11 , 1448 - 1462
22. 22)
  - M. Xu , J. Orwell , L. Lowey , D. Thirde . Architecture and algorithms for tracking football players with multiple cameras. IEE Proc. Vis. Image Signal Process. , 2 , 232 - 241

Visual tracking of partially observable targets with suboptimal filtering

Visual tracking of partially observable targets with suboptimal filtering

Buy article PDF

Buy Knowledge Pack

Thank you

References

Related content