Abstract: Falling has been one of the major concerns and threats
to the independence of the elderly in their daily lives. With the
worldwide significant growth of the aging population, it is essential
to have a promising solution of fall detection which is able to operate
at high accuracy in real-time and supports large scale implementation
using multiple cameras. Field Programmable Gate Array (FPGA) is a
highly promising tool to be used as a hardware accelerator in many
emerging embedded vision based system. Thus, it is the main
objective of this paper to present an FPGA-based solution of visual
based fall detection to meet stringent real-time requirements with
high accuracy. The hardware architecture of visual based fall
detection which utilizes the pixel locality to reduce memory accesses
is proposed. By exploiting the parallel and pipeline architecture of
FPGA, our hardware implementation of visual based fall detection
using FGPA is able to achieve a performance of 60fps for a series of
video analytical functions at VGA resolutions (640x480). The results
of this work show that FPGA has great potentials and impacts in
enabling large scale vision system in the future healthcare industry
due to its flexibility and scalability.