Long term video segmentation through pixel level spectral clustering on GPUs

We introduce a new technique for performing video segmentation combining the state-of-the-art image segmentation and optical flow algorithms on GPUs. We avoid pre-clustering into superpixels and probabilistic reasoning, and instead view the problem as a generalization of image segmentation techniques. Utilizing spectral clustering techniques at the pixel level (as opposed to 2D/3D superpixels), we demonstrate video segmentation over hundreds of frames - far beyond what has been achieved through pixel level spectral segmentation techniques before. Our algorithm achieves comparable accuracy as other sparse motion clustering techniques while still maintaining 100% density in segmentation over long time periods. We achieve better accuracy with lower oversegmentation compared to dense video segmentation techniques. We exploit increased computational power made available through parallelism in GPUs and efficient numerical algorithms to achieve these results. We show our results on the motion segmentation dataset [4]. Our technique can also be used to provide good quality 3D superpixels and extended to tasks where the ability to track 3D volumes over time is useful.