Modern tracking methods typically rely on features to track objects. These methods function best with objects
containing distinguishable features. Previously we proposed a graph cuts approach that utilizes intensity changes
and the likelihood that the RGB intensities associated with a pixel belong to the object. We propose a new
method that models the RGB tuple as a single random variable. This allows for more robust segmentation, but
requires more data to construct the color model.The results show the ability of the method to tracking in a varity
environments and with a large variety of objects.
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.