Abstract
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.
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Notes
- 1.
Sequences were kindly provided by Esther Horbert from the Computer Vision Group, RWTH Aachen University.
- 2.
- 3.
No official publication available. Only a brief abstract in [19].
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Acknowledgments
We thank Esther Horbert (Computer Vision Group RWTH Aachen University) for providing four evaluation sequences and valuable feedback for resolving open questions on the hidden details of [6].
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Schubert, F., Casaburo, D., Dickmanns, D., Belagiannis, V. (2015). Revisiting Robust Visual Tracking Using Pixel-Wise Posteriors. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_26
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