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

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

WoS Article

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).

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).

camera calibration; fully automatic; traffic surveillance; bounding box alignment; vanishing point detection

camera calibration; fully automatic; traffic surveillance; bounding box alignment; vanishing point detection

SOCHOR, J.; JURÁNEK, R.; HEROUT, A.

2018

01.06.2017

1077-3142

COMPUTER VISION AND IMAGE UNDERSTANDING

2017

161

United States of America

87

98

12

https://www.fit.vut.cz/research/publication/11455/

2017-CVIU-SpeedMeasurement