Frame Differencing with Simulink model for Moving Object Detection

Visual sensor networks (VSNs) have been attracting more and more research attention nowadays. Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. One of the simplest techniques for detection is background subtraction (BS) and frame difference, which identifies moving objects from the portion of a video frame that differs significantly from a background model. BS refers to the process of segmenting moving regions from image sequences. Background subtraction is a process of separating moving foreground objects from the non-moving background. In this paper, one of the traditional background subtraction techniques which is frame differencing (FD) algorithm is conducted using Simulink model to detect moving vehicles, pedestrians in urban traffic video sequences etc. The result of moving object detection using FD is not perfect that enable this research to experimental post-processing technique which is adaptive threshold in HSV color space for outdoor environment. Index Terms — tracking, background subtraction, FD, Adaptive Threshold