Applying mean shift, motion information and Kalman filtering approaches to object tracking.

Contemporary research is developing techniques to tracking objects in videos using color features, and the mean shift (MS) algorithm is one of the best. This known algorithm is employed to find the location of an object, in image sequence, by using a coefficient called the Bhattacharyya coefficient. This coefficient is calculated through an object tracking algorithm to present the similarity in appearance between an object and its candidate model, where the best representation of an object is acquired, once this is could be maximized. However, the MS algorithm performance is confounded by color clutter in background, various illuminations, occlusion types and other related limitations. Because of such effects, the algorithm necessarily decreases the value of the Bhattacharyya coefficient, indicating reduced certainty in the object tracking. In the present research, an improved convex kernel function is proposed to overcome the partial occlusion. Afterwards, in order to improve the MS algorithm against the low saturation and also sudden light, changes are made from motion information of the desired sequence. By using both the color feature and the motion information simultaneously, the capability of the MS algorithm is correspondingly increased, in the present approach. Moreover, by assuming a constant speed for the object, a robust estimator, i.e., the Kalman filter, is realized to solve the full occlusion problem. At the end, experimental results on various videos verify that the proposed method has an optimum performance in real-time object tracking, while the result of the original MS algorithm may be unsatisfied.