Abstract
Particle filtering has come into favor in the computer vision community with the CONDENSATION algorithm. Perhaps the main reason for this is that it relaxes many of the assumptions made with other tracking algorithms, such as the Kalman filter. It still places a strong requirement on the ability to model the observations and dynamics of the systems with conditional probabilities. In practice these may be hard to measure precisely, especially in situations where multiple sensors are used.
Here, a particle filtering algorithm which uses evidential reasoning is presented, which relaxes the need to be able to precisely model observations, and also provides an explicit model of ignorance.
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References
Mathias Bauer. Approximations for decision making in the dempster-shafer theory of evidence. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, pages 73–80, 1996.
Michael J. Black and David J. Fleet. Probabilistic detection and tracking of motion discontinuities. In IEEE 7th International Conference on Computer Vision, volume 1, pages 551–558, 1999.
M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. In Proceedings ECCV, 1996.
M. Isard and A Blake. Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5–28, 1998.
R. E. Kalman. A new approach to linear filtering and prediction problems. Transactions of the ASME Journal of Basic Engineering, pages 35–45, March 1960.
Kurt Konolige. Small vision systems: Hardware and implementation. In The Eights International Symposium of Robotics Research, October 1997.
John MacCormick. Probabilistic Modeling and Stochastic Algorithms for Visual Localisation and Tracking. PhD thesis, University of Oxford, January 2000.
Robin R. Murphy. Adaptive rule of combination for observations over time. In Multisensor Fusion and Integration for Intelligent Systems, 1996.
Glenn Shafer. A Mathematical Theory of Evidence. Princeton University Press, 1976.
Thomas M. Strat. Continuous belief functions for evidential reasoning. In AAAI, pages 308–313, August 1984.
Doug Y. Suh. Transformation of mass function and joint mass function for evidence theory in the continuous domain. Journal of Mathematical Analysis and Applications, 176:521–544, 1993.
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© 2002 Springer-Verlag Berlin Heidelberg
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Eveland, C.K. (2002). Particle Filtering with Evidential Reasoning. In: Hager, G.D., Christensen, H.I., Bunke, H., Klein, R. (eds) Sensor Based Intelligent Robots. Lecture Notes in Computer Science, vol 2238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45993-6_17
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DOI: https://doi.org/10.1007/3-540-45993-6_17
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