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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Learning and Regularizing Motion Models for Enhancing Particle Filter-Based Target Tracking
verfasst von : Francisco Madrigal, Mariano Rivera, Jean-Bernard Hayet
Erschienen in: Advances in Image and Video Technology
Verlag: Springer Berlin Heidelberg
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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.