Embedded Real-Time Surveillance Using Multimodal Mean Background Modeling

Automated video surveillance applications require accurate separation of foreground and background image content. Cost-sensitive embedded platforms place real-time performance and efficiency demands on techniques to accomplish this task. In this chapter, we evaluate pixel-level foreground extraction techniques for a low-cost integrated surveillance system. We introduce a new adaptive background modeling technique, multimodal mean (MM), which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level foreground extraction techniques in terms of their computation and storage requirements, and functional accuracy for three r…