Design of a Home Surveillance System Based on the Android Platform

Abstract

Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. This work proposes a general-purpose method which combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts) and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits gray color information for both background subtraction to improve object segmentation. The approach proves fast, flexible and precise in terms of pixel accuracy. The implementation of the background subtraction algorithm is done in two domains code is written in Matlab, then using Simulink blocks sets.