A Database Model for Querying Visual Surveillance Videos by Integrating Semantic and Low-Level Features

Automated visual surveillance has emerged as a trendy application domain in recent years. Many approaches have been developed on video processing and understanding. Content-based access to surveillance video has become a challenging research area. The results of a considerable amount of work dealing with automated access to visual surveillance have appeared in the literature. However, the event models and the content-based querying and retrieval components have significant gaps remaining unfilled. To narrow these gaps, we propose a database model for querying surveillance videos by integrating semantic and lowlevel features. In this paper, the initial design of the database model, the query types, and the specifications of its query language are presented.