Channelizing Machine Learning Towards Early Threat Detection and Prevention Against Women Using Surveillance Cameras

Abstract

Abstract: Closed Circuit Television Systems (CCTV) are being used in a growing number of office buildings, housing developments, and public locations. Many towns now have CCTV surveillance systems in place. Such monitoring systems create a tremendous load for the CCTV operators because the number of cameras views a single CCTV operator can process is constrained by human considerations. Surveillance automation of public places assumes an important role in proactively detection of possible threat to public and in maintaining law and order. Based on a review of the existing approaches followed in monitoring of crowd behaviours and the techniques applied, especially in public places like bus stands, railway stations and airports, the paper proposes surveillance automation i.e., automating the process of detecting, recognizing the suspects and suspicious behaviour of the people in the crowd. Here, in this project called channelizing machine learning towards early threat detection and prevention against women using surveillance cameras, we propose algorithms that are capable of alerting the human operator when: (1) Presence of a inhuman act, after setting zones of interest and danger zones within those zones of interest, the danger is detected when someone follows a women on streets, roads or any other public places, which can reduce the number of sexual harassment and other forms of sexual Algorithms violence in public spaces; (2) An abnormal behaviour of a person such as handling some objects abnormally; (3) In this project, our main aim is towards women safety and also we concentrate on reducing the amount of false alerts and to enable a real-life use of the system.