Publication

Abstract : Limited memory, energy and bandwidth are the major constraints in wireless visual sensor network (WVSN). Video surveillance applications in WVSN attracts a lot of attention in recent years which requires high detection accuracy and increased network lifetime that can be achieved by reducing the energy consumption in the sensor nodes. Compressed sensing (CS) based background subtraction plays a significant role in video surveillance application for detecting the presence of anomaly with reduced complexity and energy. This paper presents a system based on CS for single and multi object detection that can detect the presence of an anomaly with higher detection accuracy and minimum energy. A novel and efficient mean measurement differencing approach with adaptive threshold strategy is proposed for detection of the objects with less number of measurements, thereby reducing transmission energy. The performance of the system is evaluated in terms of detection accuracy, transmission energy and network lifetime. Furthermore, the proposed approach is compared with the conventional background subtraction approach. The simulation results show that the proposed approach yields better detection accuracy with 90% reduction in samples compared to conventional approach.