Video Segmentation and Semantics Extraction from the Fusion of Motion and Color Information

In recent years, digital multimedia technologies have evolved significantly, and are finding numerous applications, over the internet, and even over mobile networks. Thus, the video processing community has started focusing more intensively on the extraction of higher level information from multimedia data. This paper proposes a novel two-stage video processing system that aims to segment and extract semantically meaningful information, which can help achieve higher level interpretation of video. The flow fields present in the video are accumulated over several frames and their statistics are processed to derive an "activity area", that is characteristic of the type of events taking place. The color information complements the motion data, and is used for the accurate segmentation of the moving entities in each frame. The joint use of the activity area and accurate segmentation can serve as a first step to the further semantic interpretation of the video, including the recognition and accurate localization of moving objects of interest. We present experiments that demonstrate the effectiveness of our method for real videos.