Abstract
Condensation is a widely-used tracking algorithm based on particle filters. Although some results have been achieved, it has several unpleasant behaviours. In this paper, we highlight these misbehaviours and propose two improvements. A new weight assignment, which avoids sample impoverishment, is presented. Subsequently, the prediction process is enhanced. The proposal has been successfully tested using synthetic data, which reproduces some of the main difficulties a tracker must deal with.
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Rowe, D., Rius, I., Gonzàlez, J., Roca, X., Villanueva, J.J. (2005). Probabilistic Image-Based Tracking: Improving Particle Filtering. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_11
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DOI: https://doi.org/10.1007/11492429_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26153-7
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