Scalable independent multi-level distribution in multimedia content analysis

Abstract

Due to the limited processing resources available on a typical host, monolithic multimedia content analysis applications are often restricted to simple content analysis tasks, covering a small number of media streams. This limitation on processing resources can often be reduced by parallelizing and distributing an application, utilizing the processing resources on several hosts. However, multimedia content analysis applications consist of multiple logical levels, such as streaming, filtering, feature extraction, and classification. This complexity makes parallelization and distribution a difficult task, as each logical level may require special purpose techniques. In this paper we propose a component-based framework where each logical level can be parallelized and distributed independently. Consequently, the available processing resources can be focused on the processing bottlenecks at hand.An event notification service based interaction mechanism is a key factor for achieving this flexible parallelization and distribution. Experiments demonstrate the scalability of a real-time motion vector based object tracking application implemented in the framework.