ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 191-200, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
02 Jun 2016
S. Hosseinyalmdary and A. Yilmaz Photogrammetric Computer Vision Laboratory (PCVLab), Civil and Environmental Engineering and Geodetic Science Department, The Ohio State University 2070 Neil avenue, 43210 Columbus, OH, USA
Keywords: Traffic Light Detection, Conic Section, Homography Transformation, OpenStreetMap Abstract. Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.
Conference paper (PDF, 10077 KB)

Citation: Hosseinyalmdary, S. and Yilmaz, A.: TRAFFIC LIGHT DETECTION USING CONIC SECTION GEOMETRY, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 191-200,, 2016.

BibTeX EndNote Reference Manager XML