Particle filter based visual tracking using new observation model

A new tracker based on particle filter is proposed in this paper. In our framework, colour cue and edge cue, which are represented as colour histogram (CH) and improved histogram of oriented gradient (IHOG) respectively, are adaptively fused to represent the target observation. Colour histogram is robust to shape variation and rotation etc, but sensitive to varying illumination and easy to be confused by distractions from background due to loss of spatial information; whereas for IHOG, the situation is reversed. With the help of the complementary nature of the two kinds of image features, the proposed tracker is more robust to pose variations, illumination changes and distractions from background. As the second contribution of this paper, an improved model update scheme is proposed to address the varying appearance. The new scheme makes our object model has a better resistance to template drift. Experimental results demonstrate the high robustness and effectiveness of our method in complex environments.