Based On The Face Of Predicted Mean Shift Tracking Algorithm

Among many methods about face tracking technology, Mean Shift is a nonparametric estimation and high effective pattern matching algorithm. Since the algorithm can be easily combined with other algorithm to build good tracker, it has been extensively studied and widely used in the object tracking.The shortcoming of a Mean Shift based face tracking system is it uses only the complexion information but no motion information. When the occlusion occurs, the object is easily lost resulting in the tracking failure. In order to improve the tracking ability, two improved mean shift algorithms are proposed in this thesis:The first one is Mean-Shift combined with Kalman filter tracking algorithm, where a Kalman filter is employed to predict the object position in current frame. As a result, the searching area of the Mean-Shift is diminished and the tracking speed is improved. The Experiment results show that the proposed algorithm can not only track the fast moving object well, but also deal with the occlusion efficiently.Because Kalman filter is a linear predictor, it cannot be used in complex motion prediction. Therefore, another face tracking algorithm, particle filter based mean shift face tracking scheme is proposed. The Mean-Shift algorithm converge re-sampling particles to candidate areas of objects. The required particle number is reduced greatly and the real-time ability is improved. Experiment results demonstrate that the improved algorithm can steadily track fast moving objects in the complicated background and temporally occluded objects.