Design of Real-Time Self-Frame-Rate-Control Foreground Detection for Multiple Camera Surveillance System

Abstract

Emerging video surveillance technologies are based on foreground detection to achieve event detection automatically. Integration foreground detection with a modern multi-camera surveillance system can significantly increase the surveillance efficiency. The foreground detection often leads to high computational load and increases the cost of surveillance system when a mass deployment of end cameras is needed. This paper proposes a DSP-based foreground detection algorithm. Our algorithm incorporates a temporal data correlation predictor (TDCP) which can exhibit the correlation of data and reduce computation based on this correlation. With the DSP-oriented foreground detection, an adaptive frame rate control is developed as a low cost solution for multi-camera surveillance system. The adaptive frame rate control automatically detects the computational load of foreground detection on multiple video sources and adaptively tunes the TDCP to meet the real-time specification. Therefore, no additional hardware cost is required when the number of deployed cameras is increased. Our method has been validated on a demonstration platform. Performance can achieve real-time CIF frame processing for a 16-camera surveillance system by single-DSP chip. Quantitative evaluation demonstrates that our solution provides satisfied detection rate, while significantly reducing the hardware cost.