Abstract
Visual detection and target tracking are interdisciplinary tasks oriented to estimate the state of moving objects in an image sequence. There are different techniques focused on this problem. It is worth highlighting particle filters and Kalman filters as two of the most important tracking algorithms in the literature. In this paper, we presented a visual tracking algorithm which combines the particle filter framework with memory strategies to handle occlusions, called as memory-based particle filter (MbPF). The proposed algorithm follows the classical particle filter stages when a confidence measurement can be obtained from the system. Otherwise, a memory-based module try to estimate the hidden target state and to predict its future states using the process history. Experimental results showed that the performance of the MbPF is better than a standard particle filter when dealing with occlusion situations.
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Montemayor, A.S., Pantrigo, J.J., Hernández, J. (2009). A Memory-Based Particle Filter for Visual Tracking through Occlusions. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Bioinspired Applications in Artificial and Natural Computation. IWINAC 2009. Lecture Notes in Computer Science, vol 5602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02267-8_30
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DOI: https://doi.org/10.1007/978-3-642-02267-8_30
Publisher Name: Springer, Berlin, Heidelberg
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