@inproceedings{823fe8febeeb46b59a78008be2908abc,
title = "Video analysis for improving transportation safety. obstacles and collision detection applied to railways and roads",
abstract = "Obstacles detection systems are essential to achieve a higher level of safety on railways. Such systems should have the ability to contribute to the development of automated guided trains. Even though some laser equipments have been used to detect obstacles, short detection distance and low accuracy on curve zones make them not the best solution. In this paper, computer vision combined with prior knowledge is used to develop an innovative approach. A function to find the starting point of the rails is proposed. After that bottom-up adaptive windows are created to focus on the region of interest and ignore the background. The whole system can run in real time thanks to its linear complexity. It performs well in different conditions and it can work both on online and offline recorded video.",
keywords = "Computer vision, Obstacles detection, Prior Knowledge, Transportation, Video Forensic",
author = "Hui Wang and Xiaoquan Zhang and Lorenzo Damiani and Pietro Giribone and Roberto Revetria and Giacomo Ronchetti",
year = "2017",
month = apr,
day = "1",
language = "English",
volume = "2",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "909--915",
editor = "Oscar Castillo and Ao, {S. I.} and Craig Douglas and Feng, {David Dagan} and Korsunsky, {A. M.}",
booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017",
address = "Hong Kong",
note = "2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 ; Conference date: 15-03-2017 Through 17-03-2017",
}