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Unsupervised learning-based long-term superpixel tracking

Abstract : Finding correspondences between structural entities decomposing images is of high interest for computer vision applications. In particular, we analyze how to accurately track superpixels - visual primitives generated by aggregating adjacent pixels sharing similar characteristics - over extended time periods relying on unsupervised learning and temporal integration. A two-step video processing pipeline dedicated to long-term superpixel tracking is proposed. First, unsupervised learning-based superpixel matching provides correspondences between consecutive and distant frames using new context-rich features extended from greyscale to multi-channel and forward-backward consistency constraints. Resulting elementary matches are then combined along multi-step paths running through the whole sequence with various inter-frame distances. This produces a large set of candidate long-term superpixel pairings upon which majority voting is performed. Video object tracking experiments demonstrate the accuracy of our elementary estimator against state-of-the-art methods and proves the ability of multi-step integration to provide accurate long-term superpixel matches compared to usual direct and sequential integration.
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Submitted on : Wednesday, July 20, 2022 - 2:31:00 PM
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Pierre-Henri Conze, Florian Tilquin, Mathieu Lamard, Fabrice Heitz, Gwénolé Quellec. Unsupervised learning-based long-term superpixel tracking. Image and Vision Computing, Elsevier, 2019, 89, pp.289-301. ⟨10.1016/j.imavis.2019.06.011⟩. ⟨hal-02189986⟩

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