Automatic object extraction in single-concept videos

Abstract

We propose a motion-driven video object extraction (VOE) method, which is able to model and segment foreground objects in single-concept videos, i.e. videos which have only one object category of interest but may have multiple object instances with pose, scale, etc. variations. Given such a video, we construct a compact shape model induced by motion cues, and extract the foreground and background color information accordingly. We integrate these feature models into a unified framework via a conditional random field (CRF), and this CRF can be applied to video object segmentation and further video editing and retrieval applications. One of the advantages of our method is that we do not require the prior knowledge of the object of interest, and thus no training data or predetermined object detectors are needed; this makes our approach robust and practical to real-world problems. Very attractive empirical results on a variety of videos with highly articulated objects support the feasibility of our proposed method.