This paper describes an original strategy for using a data-driven probabilistic motion model into particle filter-based target tracking on video streams. Such a model is based on the local motion observed by the camera during a learning phase. Given that the initial, empirical distribution may be incomplete and noisy, we regularize it in a second phase. The hybrid discrete-continuous probabilistic motion model learned this way is then used as a sampling distribution in a particle filter framework for target tracking. We present promising results for this approach in some common datasets used as benchmarks for visual surveillance tracking algorithms. © 2011 Springer-Verlag.
CITATION STYLE
Madrigal, F., Rivera, M., & Hayet, J. B. (2011). Learning and regularizing motion models for enhancing particle filter-based target tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7088 LNCS, pp. 287–298). https://doi.org/10.1007/978-3-642-25346-1_26
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