Visual object tracking using spatial Context Information and Global tracking skills

Abstract

Tracking objects in videos by the mean shift algorithm with color weighted histograms has received much attention in recent years. However, the stability of weights in mean shift still needs to be improved especially under low-contrast scenes with complex motions. This paper presents a new type of color cue, which produces stable weights for mean shift tracking and can be computed pixel by pixel efficiently. The proposed color cue employs global tracking techniques to overcome the illustrated drawbacks of the mean shift algorithm. It represents a target candidate with a larger scale than that of the target model so that the model is much more precise than the candidate. We illustrate that the weights by this way are more reliable under various scenes. To further suppress surrounding clutters, we establish a new spatial context model so that the optimization results are a set of weights which can be computed pixel by pixel. The proposed color cue is called CIG since it computes the weights based on spatial Context Information and Global tracking skills. Experimental results on various tracking videos show that weight images by CIG have higher stability and precision than those of current methods especially under low-contrast scenes with complex motions.