Weighted Sub-block Mean-Shift Tracking with Improved Level Set Target Extraction

Mean-shift tracking algorithm is a widely-used tool for efficiently tracking target. However, the background change and shade usually lead to tracking errors and low tracking accuracy. In this paper, we introduce a novel mean-shift tracking algorithm based on weighted sub-block which incorporates the improved level set target extraction. The weight of each sub-block is determined by the similarity of target and candidate sub-blocks, and by the ratio of the target sub-block and overall areas. The target sub-block area is calculated by the means of the narrow band level set combined with a compromise to improve extraction accuracy and operating efficiency. Both of RGB color information in the target region and the pixel´s position information are taken into consideration while describing the feature model of target and candidate region inside each sub-block. Experimental results demonstrate the method´s success for tracking of targets with background change and shade during the dynamic scene, where the basic mean-shift tracking algorithm fails. The proposed method has better tracking performance with higher tracking accuracy and adaptability.