@inproceedings{4aec8164789d4cb0baafbaf09a820d4b,
title = "Enhanced pixel-wise voting for image vanishing point detection in road scenes",
abstract = "Vanishing point estimation is a crucial task in vision-based road detection. This paper presents a new texture-based voting scheme, which enhances both accuracy and speed of vanishing point estimation. In the proposed method, color tensors analysis is adopted to calculate local orientations and color edges. The search space is reduced by optimizing the set of vanishing point candidates and voters. A new strategy based on Bayesian classifier is proposed to select a suitable voting function. The proposed method is evaluated on a benchmark dataset of 4000 images of pedestrian lanes with annotated vanishing points. The experimental results show that it offers an improved accuracy and significantly faster processing time compared with other state-of-the-art methods.",
keywords = "Bayesian classifier, pixel-wise voting, vanishing point",
author = "L. Nguyen and Phung, \{S. L.\} and A. Bouzerdoum",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952477",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1852--1856",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}