AIOE BASED REAL TIME THREAT DETECTORS FOR SMART SURVEILLANCE

Abstract: A distributed AI-Powered system that can help security personnel detect various types of weapons in real time. The deep learning architecture interfaces uses single shot detection to allow users to interact with the threat detector system conveniently at the camera and cloud sides. A motion detection module is proposed for detecting moving objects in surveillance videos in real - time. The developed module is integrated seamlessly with both the camera and cloud sides. Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This project implements automatic weapon detection using a convolution neural network (CNN) using Alexnet. Proposed implementation uses datasets, which had pre-labelled images. Results are tabulated, achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.

Keywords: AI power system, image processing, MatLab, M-files . CNN