Learning and regularizing motion models for enhancing particle filter-based target tracking

Authors:
Francisco Madrigal;Mariano Rivera;Jean-Bernard Hayet
Affiliations:
Centro de Investigación en Matemáticas, Guanajuato, GTO, México;Centro de Investigación en Matemáticas, Guanajuato, GTO, México;Centro de Investigación en Matemáticas, Guanajuato, GTO, México
Venue:
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Year:
2011

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Abstract

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.