Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications

Abstract

Increased crime in packed events or lonely areas has made security a top priority in every industry. Computer Vision is used to find and fix anomalies. Increasing needs for security, privacy, and private property protection require video surveillance systems that can recognize and understand scene and anomalous situations. Monitoring such activities and recognizing antisocial behavior helps minimize crime and social offenses. Existing surveillance and control systems need human oversight. We're interested in detecting firearms quickly through photos and surveillance data. We recast the detection problem as decreasing false positives and solve it by building a data set guided by a deep CNN classifier and evaluating the best classification model using the region proposal approach. Our model uses Faster RCNN, YOLO.