In this paper, a visual tracking algorithm is proposed by combining particle filter with grey prediction model.
Particle filter does not take into account the guidance of historical prior on the generation of proposal distribution, so that it
can not approximate posterior density well. Therefore, the history of state estimation sequence is utilized as prior information
to set up grey prediction model for predicting and generating proposal distribution. Through the comparison to particle filter,
Kalman particle filter and unscented particle filter, the proposed algorithm exhibits better performance in visual tracking.