Multimodal Abandoned/Removed Object Detection for Low Power Video Surveillance Systems

Low-cost and low-power video surveillance systems based on networks of wireless video sensors will enter soon the marketplace with the promise of flexibility, quick deployment and providing accurate and real-time visual data. Energy autonomy and efficiency of the implemented algorithms are undoubtedly the primary design challenges to be addressed on systems subject to low computational capabilities and memory constraints. In this paper we present a low-power video sensor node designed for low-cost video surveillance which is able to detect abandoned and removed objects. The system exploits multi-modal sensor integration which saves on-board power consumption. In particular a pyroelectric infrared (PIR) sensor is exploited to optimize the use of the camera, grabbing images only when required in order to obtain the maximum efficiency from event recognition. Our fixed-point ARM-based approach is characterized in terms of runtime execution and power consumption, while efficiency is demonstrated by experimental results and compared with floating point implementations.