Learning Dense Optical-Flow Trajectory Patterns for Video Object Extraction

We proposes an unsupervised method to address videoobject extraction (VOE) in uncontrolled videos, i.e. videoscaptured by low-resolution and freely moving cameras. Weadvocate the use of dense optical-flow trajectories (DOTs),which are obtained by propagating the optical flow informationat the pixel level. Therefore, no interest point extractionis required in our framework. To integrate colorand and shape information of moving objects, we groupthe DOTs at the super-pixel level to extract co-motion regions,and use the associated pyramid histogram of orientedgradients (PHOG) descriptors to extract objects of interestacross video frames. Our approach for VOE is easy to implement,and the use of DOTs for both motion segmentationand object tracking is more robust than existing trajectorybasedmethods. Experiments on several video sequencesexhibit the feasibility of our proposed VOE framework.