A novel video data model for moving object description based on spatio-temporal relations

Video processing has been more and more concentrated on moving objects in the video. Video objects refer to semantic real-world entity definitions that are used to denote a coherent spatial-temporal region and be automatically computed by the continuity of spatial-temporal low-level features, such as color and motion. So, in this paper, we propose a video data model to describe events and actions performed by moving objects. This model is flexible, to support the moving objects spatio-temporal relations for semantic concepts description and low-level feature extraction. In order to cater to the users´ query by video content instead of raw data, we decompose the video object action (VOA) at semantic level into an elementary video object motion (EVOM) for extracting the low-level features. The video data model, based on moving objects, can bridge the gap between semantics and low-level features.