Machine Learning Based Abnormal Human Behaviour Detection

Due to technological advancements, numerous surveillance cameras has been installed in our everyday living spaces to enhance security measures. Assessing abnormalities within video recordings, particularly in crowded environments, presents a formidable challenge. Anomalous occurrences, arising from infrequent and uncommon behaviours, are characterized by deviations in nearby spatiotemporal positions. To bolster public safety, surveillance cameras are frequently deployed in crowded areas such as hospitals, banks, and shopping districts. The proposed system combines You Only Look Once (YOLO) and 2D convolution layer (CONV2d) to efficiently detect unconventional human activities and abnormalities in real-time video footage. Employing computer vision and machine learning techniques, it scrutinizes video frames to identify potential threats or risks through the detection of abnormal behaviours. YOLO facilitates instantaneous object detection, while CONV2d effectively processes and analyses image data. By leveraging these technologies, the system is capable of monitoring and identifying human behaviour, thus enabling the real-time detection of abnormalities and potential threats. However, challenges persist regarding the placement of security cameras and the insufficient number of cameras compared to human monitors. Identifying abnormal events, such as crimes, illegal activities, and traffic accidents, remains a paramount duty in video surveillance and our proposed system strives to achieve improved accuracy in real-time event identification.