Hawk-Eye: An AI-Powered Threat Detector for Intelligent Surveillance Cameras
Hawk-Eye: An AI-Powered Threat Detector for Intelligent Surveillance Cameras
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DOI：
10.1109/access.2021.3074319
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Echi, Mathias
中科院分区：
文献类型：
--
作者：
Ahmed, Ahmed Abdelmoamen;Echi, Mathias
随着人工智能和物联网功能的最新进展，比以往任何时候都有可能实施监控系统，自动识别可能对公众构成潜在安全威胁的人。想象一下，一个监控摄像头系统可以探测到各种身上的武器、蒙面的面孔、可疑物体和交通状况。该系统可以将监控摄像头从被动的哨兵转变为主动的观察者，这将有助于防止在学校、体育场或商场可能发生的大规模枪击事件。在本文中，我们提出了这种系统的原型实现，鹰眼，一种用于智能监控摄像机的人工智能威胁探测器。鹰眼可以部署在云端的集中式服务器上，也可以部署在网络边缘的本地监控摄像机上。在边缘部署支持人工智能的监控应用程序，可以在现场对捕获的图像进行初步分析，从而减少通信开销并实现快速的安全行动。在云端，我们建立了一个Mask R-CNN模型，可以在边缘的相机捕获的图像中检测到可疑物体。该模型可以为图像中的每个对象实例生成高质量的分割掩码，以及置信度百分比和分类时间。摄像头侧使用了树莓派3设备、英特尔神经计算棒2 （NCS 2）和罗技C920网络摄像头。在摄像头方面，我们建立了一个CNN模型，它可以直接从现场的网络摄像头中消费图像流，对它们进行分类，并通过gui友好的界面向用户显示结果。开发了运动检测模块，当检测到新的运动时，自动从视频中捕获图像。最后，我们使用各种性能指标（如分类时间和准确性）评估我们的系统。我们的实验结果显示，在我们的数据集上，平均整体预测准确率为94%。
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 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 performance metrics such as classification time and accuracy. Our experimental results showed an average overall prediction accuracy of 94% on our dataset.