Emerging smart cities are typically equipped with thousands of outdoor cameras. However, these cameras are typically not calibrated, i.e., information such as their precise mounting height and orientation is not available. Calibrating these cameras allows measurement of real-world distances from the video, thereby, enabling a wide range of novel applications such as identifying speeding vehicles, city road planning, etc. Unfortunately, robust camera calibration is a manual process today and is not scalable.

In this paper, we propose AutoCalib, a system for scalable, automatic calibration of trafc cameras. AutoCalib exploits deep learning to extract selected key-point features from car images in the video and uses a novel filtering and aggregation algorithm to automatically produce a robust estimate of the camera calibration parameters from just hundreds of samples. We have implemented AutoCalib as a service on Azure that takes in a video segment and outputs the camera calibration parameters. Using video from realworld trafc cameras, we show that AutoCalib is able to estimate real-world distances with an error of less than 12%.