A Video Representation Using Temporal Superpixels

We develop a generative probabilistic model for temporally consistent superpixels in video sequences. In contrast to supervoxel methods, object parts in different frames are tracked by the same temporal superpixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate superpixels in an online fashion. We consider four novel metrics to quantify performance of a temporal superpixel representation and demonstrate superior performance when compared to supervoxel methods.