Multiple features fusion targets tracking method based on particle filter

In view of using single feature may lead to poor robustness in tracking process under complex background, we proposed a new visual target tracking arithmetic with fusing color and edge feature by levels in particle filter (PF) frame work. The state of target was estimated approximately in frame of PF, choosing color feature or edge feature as observed value, constructing proposed distribution of the first level PF to let the particle set distributed closely around the real target state. By importance sampling on the proposed distribution, combined with the marginal feature, we got the posterior probability distribution of the second level PF. In order to overcome mistake-tracking of template drift, adaptive template update mechanism was used to tracking target. Experiment results show the proposed method, comparing with the single characteristics video object tracking, can effectively avoid the influence of blocking, attitude change and non-plane-rotation, having stronger robustness in complex sense.