Understanding images of surveillance devices in the wild

Closed-circuit television (CCTV) systems and surveillance devices have been widely used for monitoring the real world (e.g., streets, lanes, buildings). Illumination of the nature of surveillance devices' images can help in deterring criminal activity and provide insights for improving the infrastructure safety of CCTV systems. However, millions of outdated surveillance devices are still in use today, which suffer noise, the field of view, low-light conditions, and low visual quality. In this work, we conduct a large-scale empirical study of images of surveillance devices in the wild. For data collection, we deploy a web crawler to gather webcam images in the wild covering 100 websites that distribute the live streams to the public, such as a plaza and an industrial area. To enhance the low-light quality of images, we propose a new framework called ImCam for enhancing the quality of webcam images in the wild, including the retinex model and generative adversarial network (GAN). We develop a prototype of ImCam and evaluate its effectiveness over three classification systems, AlexNet, ResNet, and Vision Transformer (ViT), and Machine Learning as a Service (MLaaS) in three cloud service providers (Azure, Baidu, and Tencent). Our results show that ImCam is capable of improving low-light images for CCTV systems, making them acceptable in practice. Finally, leveraging 203,786 live streams and our ImCam, we conduct an analysis of webcam images in the wild and shed light on the nature of security usage for CCTV systems.