Low Power Architecture Exploration for Standalone Fall Detection System Based on Computer Vision

For a standalone Fall Detection system based on computer vision we want to obtain a low power architecture to meet the real time processing, power consumption, energy constraints which also satisfy the high performance in recognition, and accuracy. In this paper, we present the different architecture explorations for Fall Detection system implemented on heterogeneous platform as Zynq-7000 AP SoC platform. We extract the power models based on measurement to have more accuracy for Fall Detection system. The estimation of execution time was taking on Pcore processor like ARM Cortex A9 to find out the candidate for accelerating on Hardware (FPGAs) implementation. Then we analyze the features of power consumption, frame rate, and energy to get the best compromise architecture for standalone Fall Detection system.