Robust visual tracking based on joint multi-feature histogram by integrating particle filter and mean shift

Abstract

In this paper, we proposed a robust visual tracking and simultaneous recognition method in a particle filter frame work. First, an appearance model based on Multi-Part Joint Color Texture and Edge of Orientation Histogram with corrected background weighted histogram is Build, which is adopted to calculate the likelihood of particles; then, perform scale invariant feature transform (SIFT) keypoint detection and matching to predict the coarse position of the target; thirdly, fine tune the target position by enlarged searching region to improve the proposal distribution of particle filter, mean shift is embedded into particle filters, in which a smaller number of samples is used to estimate the posterior distribution than conventional particle filters by shifting samples to their neighboring modes of the observation so that samples are moved to have large weights. Finally, estimating the target position. Experimental results demonstrate that this algorithm can track the object accurately in conditions of scale modifications, rotation, abrupt shifts, as well as clutter and partial occlusions occurring to the tracking object with good robustness.