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Object tracking via graph cuts

Most modern tracking techniques assume that the object comprises a large percentage of the image frame, however when the object is contained in a small number of pixels tracking via feature based methods is difficult, because they require a dense feature set which does not exist within small regions. As an alternative to dynamic boundary based methods, which require only a boundary between the object and the background, but often fail in busy enviroments, we propose using a novel graph cuts implemenation to obtain a more robust segmentation. The push-relabel method was chosen because of its lower time complexity. In addition the algorithm was expanded to the RGB color-space. This is done by a probabilistic combination of the RGB pixel values. This addition, by using all the information captured by the camera, allow objects with similar appearances and objects with large variances in color to be segmented. The final addition made to the the push-relabel algorithm is an min-cut approximation method which runs in O(n) time. We show that this formulation of the graph cut algorithm allows for a fast and accurate segmentation at 30 frames per second.