Mean Shift Tracking Based on Ensemble Multiple Instance Learning

An effective object tracking method was proposed by combining multiple instance learning and mean shift tracking. The motivation is to use the advantages of the generative model and discriminative model and use ensemble learning to gain more robust tracking effect. First, instances are randomly selected to train different classifiers in the previous frame, and then the final integrated classifier was trained using ensemble learning to improve the tracking accuracy. The initial position of the object was determined by using ensemble multiple instance learning. Then mean shift tracking was used to revise the initial position by comparing the distance between the initial position and the object position in the previous frame to a threshold. The experimental results has shown that the proposed algorithm has good performance in many complicated situations, e.g. pose change, rotation, occlusion and changes of illumination, and can track successfully for a long time with strong robustness.