A Brief Review: Border Intruder Detection System Based On Computer Vision

Abstract

Research in the video surveillance is gaining more popularity due to its widespread applications as well as social impact. In this paper, we present an intelligent framework for detection of multiple events in surveillance videos. Based on the principle of compositionality, the surveillance problems can be modularize into a set of variables comprising regions-of-interest, classes (i.e. human, vehicle), attributes (i.e. speed, locality) and a set of notions (i.e. rules) associated to each of the attributes to construct a knowledge based understanding of the environment. The final output from the reasoning process, which combines the definition domains of the various variables, can allow a broader and integrated understanding of complex pattern of activities in the scene. This is in contrast to the state-of-the-art solutions that are only able to perform only a singular task, at a time. In this paper we have briefed general idea of border surveillance and intruder detection based on computer vision applications in which CCTV cameras can be used for detecting an intruder. We have reviewed the existed work done in this field and proposed the future work to be explored by us.