Learning Dense Optical-Flow Trajectory Patterns for Video Object Extraction

We proposes an unsupervised method to address video object extraction (VOE) in uncontrolled videos, i.e. videos captured by low-resolution and freely moving cameras. We advocate the use of dense optical-flow trajectories (DOTs), which are obtained by propagating the optical flow information at the pixel level. Therefore, no interest point extraction is required in our framework. To integrate color and and shape information of moving objects, we group the DOTs at the super-pixel level to extract co-motion regions, and use the associated pyramid histogram of oriented gradients (PHOG) descriptors to extract objects of interest across video frames. Our approach for VOE is easy to implement, and the use of DOTs for both motion segmentation and object tracking is more robust than existing trajectory-based methods. Experiments on several video sequences exhibit the feasibility of our proposed VOE framework.