ANOMALOUS BEHAVIOR TRIGGERS PUBLIC SURVEILLANCE ALERT USING NEURAL NETWORK AND FACE EXPRESSIONS

In an era marked by heightened concerns regarding public safety, the development of effective surveillance systems capable of detecting and responding to anomalous behavior is paramount. This paper proposes a novel approach to enhancing public safety through the integration of advanced facial expression and weapon detection technologies. The system described herein utilizes cutting-edge computer vision algorithms to analyze live video feeds from surveillance cameras deployed in public spaces. The proposed system operates in real-time, continuously monitoring individuals within its field of view. By leveraging facial expression recognition algorithms, the system can detect signs of distress, agitation, or other emotional State indicative of potential threats. Furthermore, the integration of weapon detection algorithms enables the system to identify the presence of firearms or other dangerous objects in the vicinity. Upon detection of anomalous behavior, such as aggressive facial expressions or the brandishing of a weapon, the system triggers an immediate alert. This alert is relayed to law enforcement agencies, security personnel, and other relevant authorities, facilitating a rapid and targeted response to potential threats. Key features of the proposed system include its scalability, adaptability to diverse environments, and minimal reliance on human intervention. By harnessing the power of artificial intelligence and machine learning, this system represents a significant advancement in the realm of public safety and security.