Tracking illegal activities using video surveillance systems: a review of the current state of research

Abstract

The current state of research on the use of the neural networks under martial law to identify offenders committing illegal acts, prevent acts of terrorism, combat sabotage groups in cities, track weapons and control traffic is considered. The methods of detecting illegal actions, weapons, face recognition and traffic violations using video surveillance cameras are analysed. It is proposed to introduce the studied methods into the work of “smart” video surveillance systems in Ukrainian settlements.
 The most effective means of reducing the number of offences is the inevitability of legal liability for offences, so many efforts in law enforcement are aimed at preventing offences. Along with public order policing by patrol police, video surveillance is an effective way to prevent illegal activities in society. Increasing the coverage area of cameras and their number helps to ensure public safety in the area where they are used. However, an increase in the number of cameras creates another problem which is the large amount of video data that needs to be processed. To solve the problem of video data processing, various methods are used, the most modern of which is the use of artificial intelligence to filter a large amount of data from video cameras and the application of various video processing algorithms. The ability to simultaneously process video data from many CCTV cameras without human intervention not only contributes to public safety, but also improves the work of patrol police. The introduction of smart video surveillance systems allows monitoring the situation in public places around the clock, even if there is no police presence in the area.
 In the reviewed studies of video surveillance systems, neural networks, in particular MobileNet V2, YOLO, mYOLOv4-tiny, are used to track illegal actions, criminals and weapons, which are trained on large amounts of video and photo data. It has been found that although neural networks used to require a lot of computing power, they can now be used in IoT systems and smartphones, and this contributes to the fact that more video surveillance devices can be used to monitor the situation.