Automated Teller Machine Security and Robbery Prevention Based on Human Behaviour Analysis

The suggested approach offers a way to stop people from stealing money from ATMs. Criminals steal money from ATMs in a number of ways. The suggested approach seeks to avoid all of these tactics, including the most popular one employed by criminals: stabbing victims, even though there isn't a workable solution. The cash was taken. Preventing and early detection of ATM fraud are the project's primary goals. Our system, which was created using streams combined as a single entity to identify human activity using deep learning and ATM surveillance cameras, has offered answers for some of the common cases of various sorts of ATM fraud occurrences in different countries. Even though ATMs have security cameras, theft and other crimes still commonly occur in these machines since the cameras are not effectively integrated to catch these crimes. Several image processing methods can be used to identify a person wearing a helmet, faces that are covered, and other unusual characteristics. This study recommends a model to detect weapon detection, camera manipulation, and human behavioural analysis in order to catch the theft sooner.