Abstract
With a focus on complex environments, the present paper describes a new algorithm in scale changed object tracking through color feature. Mean shift (MS) iterative procedure is the best color-based algorithm to find the location of an object. The algorithm performance is not acceptable once tracking scale changed objects in complex environments. In this paper, the main aim is to improve the MS method, using corrected background-weighted histogram (CBWH) algorithm to reduce the interference of background in target localization. To fit the object scale change, the sum of gradient mode (SGM) is employed. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the tracked objects scale changes; 3) it is less prone to the background clutter.
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