@inproceedings{67b6a05e28b94564b6fadeaf1783d990,
title = "Vision-based lane departure detection using local hough transform and particle filter",
abstract = "Lane-departure detection is an interesting and challenging task in the field of computer vision, providing an efficient way to improve driving safety. In this paper, we present a vision based lane-departure detection algorithm using local Hough transform and particle filter. A monocular vision based experiment platform also has been designed for implementing our algorithm. There are two main contributions in our work: Firstly, a novel lane detection method using local Hough transform was proposed to obtain the lane marking coordinates location. This method was proved to apply to fast and dynamic detection results. Secondly, we designed a detecting and tracking strategy, which is implemented by a particle filter, to ensure the stability of the system. The actual roads experiments are used to test the performance of our algorithm, and the experimental results shows that the proposed method is of demonstrated feasibility and reliability on actual roads.",
keywords = "Computer vision, Driver assistance, Lane departure, Particle filter",
author = "Yong Zhu and Huachun Tan and Xiaoli Chen",
note = "Publisher Copyright: {\textcopyright} ASCE; 17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017 ; Conference date: 07-07-2017 Through 09-07-2017",
year = "2018",
doi = "10.1061/9780784480915.072",
language = "English",
series = "CICTP 2017: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation - Proceedings of the 17th COTA International Conference of Transportation Professionals",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "703--712",
editor = "Haizhong Wang and Jian Sun and Jian Lu and Lei Zhang and Yu Zhang and ShouEn Fang",
booktitle = "CICTP 2017",
address = "United States",
}