= PDF Reprint,     = BibTeX entry,     = Online Abstract

Click to download PDF version Click to download BibTeX data Clik to view abstract C. K. Chang, J. Zhao, L. Itti, DeepVP: Deep Learning for Vanishing Point Detection on 1 Million Street View Images, In: Proc. IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 1-8, May 2018. [2018 acceptance rate: 40.0%] (Cited by 4)

Abstract: We propose a novel approach to detect vanishing points in images using a convolutional neural network (CNN) trained on a newly collected Google street-view image dataset. By utilizing the camera parameters and road direction data from Google street view, we collected a total of 1,053,425 images with inferred ground-truth vanishing points, along 23 worldwide routes totaling 125,165 kilometers. We then formulate vanishing point detection as a CNN classification problem using an output layer with 225 discrete possible vanishing point locations. Experimental results show that our deep vanishing point system outperforms the state-of-the-art algorithmic vanishing point detector. We achieved 99% accuracy in recovering the horizon line and 92% in locating the vanishing point within a +/-5-degree range.

Themes: Beobots, Computer Vision


Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
This page generated by bibTOhtml on Mon Jan 27 15:06:13 PST 2020