Autonomous Anomaly Detection System for Crime Monitoring and Alert Generation

Abstract Nowadays, violence has a major impact in society. Violence metrics increasing very rapidly reveal a very alarming situation. Many violent events go unnoticed. Over the last few years, autonomous vehicles have been used to observe and recognize abnormalities in human behavior and to classify them as crimes or not. Detecting crime on live streams requires classifying an event as a crime or not a crime and generating alerts to designated authorities, who can in turn take the required actions and assess the security of the city. There is currently a need for this kind of effective techniques for live video stream processing in computer vision. There are many techniques that can be used, but Long Short-Term Memory (LSTM) networks and OpenCV provide the most accurate prediction for this task. OpenCV is used for the task of object detection in computer vision, which will take the input from either a drone or any autonomous vehicle. LSTM is used to classify any event or behavior as a crime or not. This live stream is also encrypted using the Elliptic curve algorithm for more security of data against any manipulation. Through its ability to sense its surroundings, an autonomous vehicle is able to operate itself and execute critical activities without the need for human interaction. Much crowd-based crimes like mob lynching and individual crimes like murder, burglary, and terrorism can be protected against with advanced deep learning-based Anamoly detection techniques. With this proposed system, object detection is possible with approximately 90% accuracy. After analyzing all the data, it is sent to the nearest concern department to provide the remedial approach or protect from any crime. This system helps to enhance surveillance and decrease the crime rate in society.