CryogenX

In the aftermath of the recent mass shooting in Las Vegas, our prayers are with the 59 people who lost their lives and the 527 people who were badly injured. But are our prayers sufficient? In this technology age, with machine learning and other powerful AI algorithms being invented and used heavily in systems all over the world, we took this opportunity provided by Cal Hacks to build something that has the potential to have a social impact on the world by aiming to prevent such incidents of mass shootings. Snipe is a real-time recognition system to prevent the use of illegal guns for evil purposes. Firstly, the system analyses real-time video feed provided by security cams or cell phone by sampling frames and detecting whether is there a person holding a gun/rifle in the frames. This detects a suspicious person holding a gun in public and alerts the watching security. Most of the guns used for malicious intent are bought illegally, this system can also be used to tackle this scenario (avoidance scenario). Secondly, it also detects the motion of the arm before someone is about to fire a gun. The system then immediately notifies the police and the officials via email and SMS, so that they can prepare to mobilize without delay and tackle the situation accordingly. Thirdly, the system recognizes the sound of a rifle/gun being fired and sounds an alarm in nearby locations to warn the people around and avoid the situation. This system meant to aid the law enforcement to be more efficient and help people avoid such situations. This is an honest effort to use our knowledge in computer science in order to create something to make this world a better place. Our system relies on OpenCV to sample and process the real-time video stream for all machine learning components to use. 1) Image Recognition/ Object Detection: This component uses the powerful Microsoft's Cognitive Services API and Azure to classify the frames. We trained the model using our custom data images. The sampled frames are classified on the cloud telling us whether is there a gun bearing human in the image with a probability distribution. 2) Arm Motion recognition: The Arm Motion detection detects whether a person is about to fire a gun/rifle. It also uses the Microsoft's custom vision cognitive API to detect the position of the arms. We use an optimized algorithm on the client side to detect whether the returned probability distribution from API represents the gun firing motion. 3) Sound Detection: Finally to detect the sound of a rifle/gun been fired, we use NEC's sound recognition API to detect the same. Once the program detects that the API returned true for the sound chunk, we sound an alarm to warn people. The entire application was built in C++.

We are Students at USC pursuing a Master's degree in Computer Science.