An area efficient FPGA implementation of moving object detection and face detection using adaptive threshold method

TL;DR: An area efficient moving object and face detection system is designed using Spartan 6 FPGA kit and the use of adaptive threshold concept results in better detection process with PSNR of 32.39.

Abstract: The world is marching towards digital communication and digital video surveillance is an indispensable part of it. Moving object detection and face detection play an important role in the video surveillance applications. It is a part of security oriented applications such as traffic systems, hospitals, banks. Background subtraction algorithm is used in order to detect the foreground. Due to the light intensity variations in dynamically changing environment, background subtraction module needs an adaptive threshold value for each input video frame. The pixel values above the threshold value are detected as foreground pixels. The range of adaptive threshold values usually varies from 10 to 85 in the proposed work. This gives a better detection capability in moving object detection wherein PSNR of 32.39 is obtained when two detected images with and without adaptive threshold are chosen as inputs to the PSNR block. Face detection is treated as an extension module of moving object detection wherein the input face image is subtracted from the test image with the help of adaptive threshold but finally the matching unit is the additional component which decides the match based on the global threshold value which is chosen as 183000 here. Here, instead of detecting the moving body, only face is considered for detection. Since real time videos need to be processed using FPGA kits, an area efficient moving object and face detection system is designed using Spartan 6 FPGA kit. The number of slice registers used has reduced from 365 to 320 in case of moving object detection, from 861 to 537 in case of face detection. The results obtained reveal that the use of adaptive threshold concept results in better detection process with PSNR of 32.39.