The service architecture of real-time video analytic system

Videos are an important source of information and require extremely computation expensive analysis in order to understand the high level semantics. The computational difficulties in extracting embedded information and bridging the semantic gap present the major challenges in interoperability, scalability and real-time response of video analytic systems. In this paper, we propose an intelligent video analytic system VIP (Video Intelligence Platform) that provides a distributed scalable infrastructure for supporting near real-time video stream analysis. VIP is based on service-orient architecture (SOA), in which the video analysis computation modules are wrapped as services and composed in a directed acrylic graph (DAG) structure to represent the application requirements. VIP leverages UIMA (Unstructured Information Management Architecture) framework as the data flow control engine and multiple commodity databases as the storage and computation resources. The actual executions of video analysis have been pushed down into database engine to minimize the data movement cost. We initially choose video surveillance in retail store as a representative application domain. As a case study, a prototype system has been developed to achieve fundamental functions of real-time video surveillance, including video capture/store, human detection/tracking and customer shopping trajectory analysis.