CAMSHIFT improvement on multi-hue and multi-object tracking

CAMSHIFT (Continuously Adaptive Mean-Shift) has been well accepted as one of the prominent methods in object tracking. CAMSHIFT is good for single hue object tracking and in the condition where object's color is different with background's color. In this paper, we enhance CAMSHIFT so it can be used for multi-object tracking and improve the robustness of CAMSHIFT for multi-hue object tracking especially in the situation where object's colors are similar with background's colors. We propose a more precise object localization by selecting each dominant color object part using a combination of Mean-Shift segmentation and region growing. Hue-distance, saturation and value color histogram are used to describe the object. We also track the dominant color object parts separately and combine them together to improve robustness of the tracking on multi-hue object. For multi-object tracking, we use a separate tracker for each object. Our experiments showed that those methods improved CAMSHIFT robustness significantly and enable CAMSHIFT for multi-object tracking.