Abnormal Activity Detection Using CNN Method

Abstract

Anomaly Activity is the prediction of a suspicious activity from a picture or video. This project would include using neural networks to detect suspicious human activity from real-time CCTV video. Human Anomaly Activity is a central issue in computer vision that has been researched for over 15 years. It is significant due to the large number of applications that can benefit from Activity detection. Human pose estimation, for example, is used in applications such as video monitoring, animal tracking and behavior recognition, sign language identification, advanced human-computer interaction, and marker less motion recording. Low-cost depth sensors have disadvantages such as being restricted to indoor use, and their low resolution and noisy depth information make estimating human poses from depth images difficult. As a result, we want to use neural networks to solve these issues. Suspicious human activity detection in surveillance video is an active field of image processing and computer vision science. Human activities in sensitive and public areas such as bus stations, train stations, airports, banks, shopping malls, schools and colleges, parking lots, highways, and so on can be monitored using visual surveillance to detect terrorism, robbery, accidents and illegal parking, vandalism, fighting, chain snatching, violence, and other suspicious activities. It is extremely difficult to continuously track public places; thus, intelligent video surveillance is needed that can monitor human activities in real-time, classify them as normal or unusual, and generate an alarm. The majority of the analysis being conducted is on photographs rather than recordings. Furthermore, none of the papers reported attempt to use CNNs to detect suspicious activity.