Real-time human suspicious activity detection system with deep learning for improved security monitoring from live video streaming

Abstract

Security is a significant concern in today’s environment, as unusual activity frequently indicates potential threats and concerns. An abnormality, defined as something that deviates from the expected or normal, can serve as an early warning sign of criminal activity. While crime cannot be anticipated with accuracy, diligent observation of suspect behavior and circumstances can aid in predicting its occurrence. Effective surveillance systems are critical in light of the increasing number of occurrences involving people or groups utilizing firearms to injure or kill. Closed-Circuit Television systems are extensively employed to monitor settings in order to prevent crime, but constant manual surveillance of public locations is difficult. This has resulted in the necessity for intelligent video surveillance, which provides benefits such as effective monitoring, reduced labor, cost efficiency, and the adoption of new security trends. However, human behavior is inherently unpredictable, making it difficult to discriminate between suspicious and typical activity. Early identification of portable weapons such as guns, knives, screw drivers etc . are critical for fast response by security officers, potentially lowering violent crimes and homicides. So, in this work a real time human suspicious Monitoring System is proposed using deep learning technique to detect human suspicious activity involving in thefts, crimes and immediately notify law enforcement personnel, thereby improving overall security and safety from the live video stream. This work suggests a real-time method for monitoring suspicious human activity that makes use of the ESP32-CAM and deep learning. The system uses the Haar Cascade model to recognise facial expressions such as sadness, anger and fear. The Yolo model has already been trained to recognise weapons like as knives and guns. The system predicts suspicious conduct such as murder based on the detected weapons and facial expressions. The system predicts theft based on the facial expression. It identifies whether the person is wearing mask and predict the person as suspicious. It notifies law enforcement or authorised people via email when a high-probability threat is detected. The system is monitored using an intuitive graphical user interface (GUI), which shows threat statuses, activity logs, and probability-based evaluations for effective management. This method improves on conventional security systems by offering a strong framework for real-time threat identification and response.