We show how pixel-based methods can be applied to a sparse image representation resulting from a superpixel segmentation. On this sparse image representation we only estimate a single motion vector per superpixel, without working on the full-resolution image. This allows the accelerated processing of high-resolution content with existing methods. The use of superpixels in optical flow estimation was studied before, but existing methods typically estimate a dense optical flow field – one motion vector per pixel – using the full-resolution input, which can be slow. Our novel approach offers important speed-ups compared to dense pixel-based methods, without significant loss of accuracy.
CITATION STYLE
Donné, S., Aelterman, J., Goossens, B., & Philips, W. (2015). Fast and robust variational optical flow for high-resolution images using SLIC superpixels. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9386, pp. 205–216). Springer Verlag. https://doi.org/10.1007/978-3-319-25903-1_18
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