Robust model adaption for colour-based particle filter tracking with contextual information

Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using both simulated and publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research on tackling complex visual tracking problems.