With recent advances in both AI and IoT capabilities, it is possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time.Imagine a surveillance camera system that can detect Urn Element various on-body weapons, masked faces, suspicious objects and traffic.This system could transform surveillance cameras from passive sentries into active observers which would help in preventing a possible mass shooting in a school, stadium or mall.In this paper, we present a prototype implementation of such systems, Hawk-Eye, an AI-powered threat detector for smart surveillance cameras.
Hawk-Eye can be deployed on centralized servers hosted in the cloud, as well as locally on the surveillance cameras at the network edge.Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images to take place on-site, which reduces the communication overheads and enables swift security actions.At the cloud side, we built a Mask R-CNN model that can detect suspicious objects in an image captured by a camera at the edge.The model can generate a high-quality segmentation mask for each object instance in the image, along with the confidence percentage and classification time.
The camera side used a Raspberry Pi 3 device, Intel Neural Compute Stick 2 (NCS 2), and Logitech C920 webcam.At the camera side, we built a CNN model that can consume a stream of images directly from an on-site webcam, classify them, and displays the results to the user via a GUI-friendly interface.A motion detection module is developed to capture images automatically from the video when a new motion is detected.Finally, we evaluated our system using various Soccer - Clothing Mens Bottoms - Shorts performance metrics such as classification time and accuracy.
Our experimental results showed an average overall prediction accuracy of 94% on our dataset.