Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement [CVIU]

Jakub Sochor, Roman Juránek, Adam Herout
GRAPH@FIT, Brno University of Technology
Corresponding authors: isochor [at]


In this paper, we focus on fully automatic traffic surveillance camera calibration which we use for speed measurement of passing vehicles. We improve over a recent state-of-the-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from an arbitrary viewpoint and it has no constraints on camera placement. We evaluate our method on recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration by detected two vanishing points method reduces the error by 50% compared to the previous state-of-the-art method. We also show that our scene scale inference method is much more precise (mean speed measurement error 1.10km/h) outperforming both state of the art automatic calibration method (error reduction by 86% — mean error 7.98km/h) and manual calibration (error reduction by 19% — mean error 1.35km/h). We also present qualitative results of automatic camera calibration method on video sequences obtained from real surveillance cameras on various places and under different lighting conditions (night, dawn, day).



author = "Jakub Sochor and Roman Juranek and Adam Herout",
title = "Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement",
journal = "Computer Vision and Image Understanding",
volume = "161",
pages = "87 - 98",
year = "2017",
issn = "1077-3142",
doi = "",
url = "",


This work was supported by The Ministry of Education, Youth and Sports of the Czech Republic from the National Programme of Sustainability (NPU II); project IT4Innovations excellence in science — LQ1602.