Abstract
We propose a simple but effective tracking-by-segmentation algorithm using Absorbing Markov Chain (AMC) on
superpixel segmentation, where target state is estimated by
a combination of bottom-up and top-down approaches, and
target segmentation is propagated to subsequent frames in
a recursive manner. Our algorithm constructs a graph for
AMC using the superpixels identified in two consecutive
frames, where background superpixels in the previous frame
correspond to absorbing vertices while all other superpixels create transient ones. The weight of each edge depends
on the similarity of scores in the end superpixels, which are
learned by support vector regression. Once graph construction is completed, target segmentation is estimated using the
absorption time of each superpixel. The proposed tracking algorithm achieves substantially improved performance
compared to the state-of-the-art segmentation-based tracking techniques in multiple challenging datasets