Low Complexity Background Subtraction for Wireless Vision Sensor Node

Wireless vision sensor nodes consist of limited resources such as energy, memory, wireless bandwidth and processing. Thus it becomes necessary to investigate lightweight vision tasks. To highlight the foreground objects, many machine vision applications depend on the background subtraction technique. Traditional background subtraction approaches employ recursive and non-recursive techniques and store the whole image in memory. This raises issues like complexity on hardware platform, energy requirements and latency. This work presents a low complexity background subtraction technique for a hardware implemented wireless Vision Sensor Node (VSN). The proposed technique utilizes existing image scaling techniques for scaling down the image. The downscaled image is being stored in internal memory of hardware platform. For subtraction operation, the background pixels are generated in real time with up a scaling technique. The performance, and memory requirements of the system is compared for four image scaling techniques including nearest neighbor, averaging, bilinear, and bicubic. The results show that a system with lightweight scaling techniques, i.e., nearest neighbor and averaging, up to a scaling factor of 8, missed on average less than one object as compared to a system which uses a full original background image. The proposed approach will reduce the memory requirement by a factor of up to 64 besides reduction in design/implementation complexity and cost as compared to background model which involve whole frame.