Suspicious Human Activity Recognition using 2D Pose Estimation and Convolutional Neural Network

Suspicious human activity detection is a major area of research and development that focuses on sophisticated machine learning techniques to reduce monitoring costs while enhancing safety. Since it is difficult for people to continually monitor public spaces, we need a real-time intelligent human activity recognition system that can identify suspicious activities. Current systems use low-accurate complex algorithms and techniques, making the system less reliable. This paper proposes a real-time suspicious human activity recognition with high accuracy by introducing a Convolutional Neural Network and using the 2D pose estimation technique to the system. This system can be used for home security, hospitals, and other areas of surveillance. Here, we are extracting skeletal images of humans from the input video frames using 2D pose estimation to identify the pose of humans in the videos. These poses are then passed to a pre-trained Convolutional Neural Network to classify different activities of humans like trespassing or not trespassing, fall or not fall, fighting, etc. After analyzing the pixels and activities, an alert can be produced through alarms, messages to phones, email the footage to the owner or security professional, and other techniques to prevent unusual activities. This system can be used in public places like shopping malls, railway stations, public roads, and even in homes, universities, and educational institutions.