We present a robust tracking method based on many-to-many image superpixel matching (MMM). Our MMM tracker represents a target and its background using two sets of superpixels. Multiple hypotheses for superpixel matching are considered for better tracking performance. For each superpixel in an input image, k matching candidates are searched in the representative sets using approximate k-NN searching. The degree of matching is measured using foreground likelihood and matching probability assignment. The superpixel matching results are projected onto a displacement confidence map that depicts the motion probabilities of all the superpixels. During the projection, the displacements confidence of the superpixels are regularized by kernel methods. We estimate the target position by searching for the maximum probability on the displacement confidence map. The experimental results confirm that our superpixel matching achieves better performance than other trackers.