Title: Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement,
Abstract: 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 method. The method is 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 arbitrary
viewpoints, since it has no constraints on camera placement. We evaluate our
method on the recent comprehensive dataset for speed measurement BrnoCompSpeed.
Experiments show that our automatic camera calibration method by detection of
two vanishing points reduces error by 50% (mean distance ratio error reduced
from 0.18 to 0.09) compared to the previous state-of-the-art method. We also
show that our scene scale inference method is more precise, outperforming both
state-of-the-art automatic calibration method for speed measurement (error
reduction by 86% -- 7.98km/h to 1.10km/h) and manual calibration (error
reduction by 19% -- 1.35km/h to 1.10km/h). We also present qualitative results
of the proposed automatic camera calibration method on video sequences obtained
from real surveillance cameras in various places, and under different lighting
conditions (night, dawn, day).