DocumentCode :
3602603
Title :
A Method to Calibrate Vehicle-Mounted Cameras Under Urban Traffic Scenes
Author :
Yaonan Wang ; Xiao Lu ; Zhigang Ling ; Yimin Yang ; Zhenjun Zhang ; Kena Wang
Author_Institution :
Dept. of Electr. & Inf. Eng., Hunan Univ., Changsha, China
Volume :
16
Issue :
6
fYear :
2015
Firstpage :
3270
Lastpage :
3279
Abstract :
We address the problem of vehicle-mounted camera calibration under urban traffic scenes regarding the fact that the traditional calibration methods are practically restricted, since the internal parameters should be calibrated in the laboratory and it is impossible for recalibration that resulted from the parameters drifting or re-focusing when driving on roads. In this paper, we propose to utilize the manual lines lying in Manhattan directions in the scenes to compute their corresponding vanishing points for camera calibration, as the urban traffic scenes are usually man-made and the important lines and signs for driving are typically lying in the Manhattan directions. For “Manhattan world” scenes, where there are plenty of lines lying in Manhattan directions, the lines in the scene are detected automatically, and the clusters corresponding to Manhattan directions are obtained using RANSAC-like methods. For the more general “quasi-Manhattan world” scenes, where only the lines in two directions can be found naturally, while the lines in the other direction are usually detected trivially or even can be hardly detected, we propose a method to estimate the lines in the third direction to improve the vanishing point estimation accuracy. The method proposed is tested on both two types of scenes, and the accuracy and practicability of this method are demonstrated. Furthermore, calibration experiments on both one image and multiple images are conducted, which show that the results can be more accurate when more images are used.
Keywords :
calibration; cameras; image processing; intelligent transportation systems; pattern clustering; random processes; road traffic; Manhattan directions; RANSAC-like methods; clusters; driving lines; driving signs; lines estimation; quasiManhattan world scenes; random sample consensus; recalibration; refocusing; road driving; urban traffic scenes; vanishing point estimation accuracy; vehicle-mounted camera calibration; Accuracy; Bayes methods; Calibration; Cameras; Image segmentation; Mathematical model; Parameter estimation; Urban areas; Camera calibration; Manhattan direction; urban traffic scene; vanishing point;
fLanguage :
English
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1524-9050
Type :
jour
DOI :
10.1109/TITS.2015.2430617
Filename :
7114275
Link To Document :
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