research-article

Railway region detection based on Haar-like features

Authors:

Feng LiuAuthors Info & Claims

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

Pages 121 - 124

https://doi.org/10.1145/2632856.2632920

Published: 10 July 2014 Publication History

Abstract

Safety is one of the most significant issues in railway operations, and obstacle on the railway regions is the most frequent reason why an accident takes place. In this paper, we present a learning based algorithm to detect railway regions, which is a fundamental and necessary research for obstacle detection on the railway. A Haar-like feature is proposed to describe railway regions, and we employ Support Vector Machine to classify railway regions from non-railway regions. The proposed method is evaluated on a number of challenging images and experiments demonstrate that the proposed method is an effective solution to railway region detection.

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Digital Library

Cited By

Kafetzis DFourfouris IArgyropoulos SKoutsopoulos I(2020)UAV-assisted Aerial Survey of Railways using Deep Learning2020 International Conference on Unmanned Aircraft Systems (ICUAS)10.1109/ICUAS48674.2020.9213928(1491-1500)Online publication date: Sep-2020
https://doi.org/10.1109/ICUAS48674.2020.9213928

Index Terms

Railway region detection based on Haar-like features
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Other conferences

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

430 pages

ISBN:9781450328104

DOI:10.1145/2632856

General Chairs:
Hanzi Wang
Xiamen University, China
,
Larry Davis
University of Maryland, USA
,
Program Chairs:
Wenwu Zhu
Tsinghua University, China
,
Stephan Kopf
University of Mannheim, Germany
,
Yanyun Qu
Xiamen University, China
,
Publications Chairs:
Jun Yu
Xiamen University, China
,
Jitao Sang
Institute of Automation, CAS, China
,
Tao Mei
Microsoft Research Asia, China

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

NSF of China: National Natural Science Foundation of China
Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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ICIMCS '14

ICIMCS '14: International Conference on Internet Multimedia Computing and Service

July 10 - 12, 2014

Xiamen, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

Kafetzis DFourfouris IArgyropoulos SKoutsopoulos I(2020)UAV-assisted Aerial Survey of Railways using Deep Learning2020 International Conference on Unmanned Aircraft Systems (ICUAS)10.1109/ICUAS48674.2020.9213928(1491-1500)Online publication date: Sep-2020
https://doi.org/10.1109/ICUAS48674.2020.9213928

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