Fast and Robust Variational Optical Flow for High-Resolution Images Using SLIC Superpixels

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.

• Type: Journal
• Year: 2015
• Where: ACIVS
• Authors: Simon Donné, Jan Aelterman, Bart Goossens, Wilfried Philips
• Keywords: ACIVS 2015, Computer Vision