The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 49–54, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-49-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 49–54, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-49-2016

  15 Jun 2016

15 Jun 2016

A NOVEL APPROACH TO CAMERA CALIBRATION METHOD FOR SMART PHONES UNDER ROAD ENVIRONMENT

Bijun Lee1, Jian Zhou1, Maosheng Ye2, and Yuan Guo1 Bijun Lee et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China
  • 2School of Geodesy and Geomatics, Wuhan University, China

Keywords: Road Lane Markers, Machine Learning, Camera Calibration, Smart Phone

Abstract. Monocular vision-based lane departure warning system has been increasingly used in advanced driver assistance systems (ADAS). By the use of the lane mark detection and identification, we proposed an automatic and efficient camera calibration method for smart phones. At first, we can detect the lane marker feature in a perspective space and calculate edges of lane markers in image sequences. Second, because of the width of lane marker and road lane is fixed under the standard structural road environment, we can automatically build a transformation matrix between perspective space and 3D space and get a local map in vehicle coordinate system. In order to verify the validity of this method, we installed a smart phone in the ‘Tuzhi’ self-driving car of Wuhan University and recorded more than 100km image data on the road in Wuhan. According to the result, we can calculate the positions of lane markers which are accurate enough for the self-driving car to run smoothly on the road.