Automatic saliency inspired foreground object extraction from videos
In this paper, we propose a saliency inspired video object extraction (VOE) method to extract and segment foreground objects of interest from videos captured by freely moving cameras. Our method aims at detecting visual and motion salient regions from an input video, and thus we integrate such cosaliency information with the associated foreground and background color models to achieve VOE. A conditional random field (CRF) is applied in our framework to automatically identify the foreground object regions based on the above features, while our method does not need any prior knowledge on the foreground objects of interest or any interaction from the users. Experiments on a variety of videos confirm that our method is able to provide quantitatively and qualitatively more satisfactory results when comparing to state-of-the-art VOE approaches.