Mean shift tracking with graph cuts based image segmentation

Abstract

In this paper, we propose a new tracking method that applies image segmentation based on graph cuts to mean shift tracking algorithm. Mean shift tracking algorithm is an iterative scheme based on comparing the color histogram of the original object in the current image frame and the color histogram of candidate regions in the next image frame. Graph cuts can be employed to efficiently solve the problem of image segmentation in computer vision. Before tracking the object in each image frame of a video sequence, the object can be separated from the uninterested background using graph cut. Thus, the mean shift algorithm can obtain a more precise object histogram model of each frame without the interference of the background-pixels. The tracking window is designated by users and the segmenting window is generated through the tracking window. The proposed method provides more reliable performance than general mean shift tracking algorithm.