Improved Mean Shift for Robust Object Tracking
نویسندگان
چکیده
In this paper, we present an improved mean shift for robust object tracking in complex environment. Traditional RGB color model used in mean shift tracker is sensitive to interference from similar background. In order to solve this problem, a new saliency-color target model is proposed through using the state-of-the-art target representation and updated background-weighed method. In addition, traditional mean shift method using fixed tracking window may cause tracking errors when target becomes close to or far away from the camera. Therefore, tracking window with self-adjust scheme is proposed in this paper. The tracking region parameters are updated through affine transform of feature corner datasets between adjacent frames. Moreover, a new prediction strategy is utilized to track the target with fast motion and partial occlusion. Experiment results demonstrate the effectiveness of proposed method, which can track object robustly under similar background, size changing, partial occlusion, etc. Copyright © 2014 IFSA Publishing, S. L.
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