SPiKeS: Superpixel-Keypoints Structure for Robust Visual Tracking
This work addresses robust visual tracking for computer vision applications, representing an incremental improvement by combining superpixels and keypoints.
The paper tackled the problem of robust visual tracking by proposing a novel Superpixel-Keypoints structure (SPiKeS) to enhance discriminative power, resulting in favorable performance against state-of-the-art methods in challenging scenarios.
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.