SPiKeS: Superpixel-Keypoints Structure for Robust Visual Tracking

TL;DR

This paper introduces SPiKeS, a novel superpixel-keypoints structure that combines shape and discriminative features for robust visual tracking, outperforming existing methods in challenging scenarios.

Contribution

The paper proposes a new superpixel-keypoints structure (SPiKeS) that enhances superpixels with keypoints for improved discriminative power in tracking.

Findings

SPiKeS-based tracker is robust against occlusion and deformation.

It outperforms state-of-the-art trackers in challenging scenarios.

The method enables efficient model updates for better robustness.

Abstract

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.