Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques

Abstract

Abnormal behavior detection from on-line/off-line videos is an emerging field in the area of computer vision. This plays a vital role in video surveillance-based applications to provide safety for humans at public places such as traffic signals, shopping malls, railway stations, etc. Surveillance cameras are meant to act as digital eyes (i.e., watching over activities at public places) and provide security. There are a number of cameras deployed at various public places to provide video surveillance, but in reality, they are used only after some incident has happened. Moreover, a human watch is needed in order to detect the person/cause of the incident. This makes surveillance cameras passive. Thus, there is a huge demand to develop an intelligent video surveillance system that can detect the abnormality/incident dynamically and accordingly raise an alarm to the nearest police stations or hospitals as per requirement. If AI-supported CCTV systems are deployed at commercial and traffic areas, then we can easily detect the incidents/crimes, and they can be traced in minimal time.