  We introduce a novel semi-supervised video segmentation approach based on an
efficient video representation, called as "super-trajectory". Each
super-trajectory corresponds to a group of compact trajectories that exhibit
consistent motion patterns, similar appearance and close spatiotemporal
relationships. We generate trajectories using a probabilistic model, which
handles occlusions and drifts in a robust and natural way. To reliably group
trajectories, we adopt a modified version of the density peaks based clustering
algorithm that allows capturing rich spatiotemporal relations among
trajectories in the clustering process. The presented video representation is
discriminative enough to accurately propagate the initial annotations in the
first frame onto the remaining video frames. Extensive experimental analysis on
challenging benchmarks demonstrate our method is capable of distinguishing the
target objects from complex backgrounds and even reidentifying them after
long-term occlusions.
