Revisiting Robust Visual Tracking Using Pixel-Wise Posteriors

In this paper we present an in-depth evaluation of a recently published tracking algorithm [6] which intelligently couples rigid-registration and color-based segmentation using level-sets. The original method did not arouse the deserved interest in the community, most likely due to challenges in reimplementation and the lack of a quantitative evaluation. Therefore, we reimplemented this baseline approach, evaluated it on state-of-the-art datasets (VOT and OOT) and compared it to alternative segmentation-based tracking algorithms. We believe this is a valuable contribution as such a comparison is missing in the literature. The impressive results help promoting segmentation-based tracking algorithms, which are currently under-represented in the visual tracking benchmarks. Furthermore, we present various extensions to the color model, which improve the performance in challenging situations such as confusions between fore- and background. Last, but not least, we discuss implementation details to speed up the computation by using only a sparse set of pixels for the propagation of the contour, which results in tracking speed of up to 200 Hz for typical object sizes using a single core of a standard 2.3 GHz CPU.