  In visual tracking, part-based trackers are attractive since they are robust
against occlusion and deformation. However, a part represented by a rectangular
patch does not account for the shape of the target, while a superpixel does
thanks to its boundary evidence. Nevertheless, tracking superpixels is
difficult due to their lack of discriminative power. Therefore, to enable
superpixels to be tracked discriminatively as object parts, we propose to
enhance them with keypoints. By combining properties of these two features, we
build a novel element designated as a Superpixel-Keypoints structure (SPiKeS).
Being discriminative, these new object parts can be located efficiently by a
simple nearest neighbor matching process. Then, in a tracking process, each
match votes for the target's center to give its location. In addition, the
interesting properties of our new feature allows the development of an
efficient model update for more robust tracking. According to experimental
results, our SPiKeS-based tracker proves to be robust in many challenging
scenarios by performing favorably against the state-of-the-art.
