Guided importance sampling based particle filtering for visual tracking

Authors:
Kazuhiko Kawamoto
Affiliations:
Kyushu Institute of Technology, Kitakyushu, Japan
Venue:
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Year:
2006

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Abstract

Linear estimation based sequential importance sampling methods for particle filters are proposed that can be used to model the rapid change of object motion in a video sequence. First a linear least–squares estimation is used to build a proposal function from observations, and then it is extended to a robust linear estimation. These sampling methods give a framework for tracking objects whose motion cannot be well modeled by a prior model. Finally a switching algorithm between the proposed method and the prior model based sampling method is proposed to achieve a filtering of both smooth and rapid evolution of the state. The ability of the proposed method is illustrated on a real video sequence involving a rapidly moving object.