Detecting the Realtime Abnormal Human Behavior and Weapon Detection Using DenseNet Algorithm

This proposed work presents an implementation of Abnormal human activities and weapon detection systems in public places using DenseNet Algorithm. By using the DenseNet algorithms enhance the accuracy of detecting suspicious behavior and ensuring real-time analysis. Our system integrates facial recognition, object detection, and activity recognition to improve security measures against fraud and theft. This system also identifies the abnormal behavior and send an alert message to the concern department to take security measures immediately. Additionally, the benefits of utilizing video data to train models, improving surveillance efficiency and reducing false alarm rates are also discussed in this proposed work. The findings highlight the transformative potential of AI-driven surveillance in enhancing bank security protocols.