Construction and Practice of Community Abnormal Behavior Intelligent Monitoring System based on Deep Learning Algorithm

As the community scale keeps growing and public security challenges become more and more severe, the drawbacks of traditional monitoring approaches in identifying and early warning of abnormal behavior are becoming more prominent. This paper centers on the establishment of an intelligent monitoring system for abnormal behavior in the community, which is based on deep learning algorithms. Initially, the surveillance cameras within the community are utilized to acquire video data in real - time and capture the temporal correlations among video frames. Subsequently, the deep learning model is employed to extract crucial features from the data and construct an intelligent monitoring system for abnormal behavior in the community. The experimental results indicate that the accuracy of the system in detecting community abnormal behavior is generally maintained above 83%, offering a powerful technical assurance for comprehensively enhancing the level of community security management.