Abstract
Linear estimation based sequential importance sampling methods for particle filters are proposed that can be used to detect the rapid change of object motion in a video sequence. First a linear least-squares estimation is used to build an importance function from observations, and then it is extended to a robust linear estimation.
These sampling methods gives 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 mehtod 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 book held by hand. In addtion it is shown that the proposed method can achieve real-time processing.
