Camera auto-calibration using zooming and zebra-crossing for traffic monitoring applications

This paper describes a camera auto-calibration system, based on monocular vision, for applications in the framework of Intelligent Transportation Systems (ITS). Using camera zoom and a very common element of urban traffic infrastructures as it is a zebra crossing, a principal point and vanishing point extraction is proposed to obtain an automatic calibration of the camera, without any prior knowledge of the scene. This calibration is very useful to recover metrics from images or apply information of 3D models to estimate 2D pose of targets, making a posterior object detection and tracking more robust to noise and occlusions. Moreover, the algorithm is independent of the position of the camera, and it is able to work with variable pan-tilt-zoom cameras in fully self-adaptive mode. In the paper, the results achieved up to date in real traffic conditions are presented and discussed.