Vehicle detection in hsuehshan tunnel using background subtraction and deep belief network

Bo Jhen Huang, Jun-Wei Hsieh, Chun Ming Tsai*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This paper proposes a method to detect vehicle in the Hsuehshan Tunnel. Vehicle detection in the Tunnel is a challenging problem due to use of heterogeneous cameras, varied camera setup locations, low resolution videos, poor tunnel illumination, and reflected lights on the tunnel wall. Furthermore, the vehicles to be detected vary greatly in shape, color, size, and appearance. The proposed method is based on background subtraction and Deep Belief Network (DBN) with three hidden layers architecture. Experimental results show that it can detect vehicles in he Tunnel effectively. The experimental accuracy rate is 96.59%.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
EditorsSatoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
PublisherSpringer Verlag
Pages217-226
Number of pages10
ISBN (Print)9783319544298
DOIs
StatePublished - 1 Jan 2017
Event9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan
Duration: 3 Apr 20175 Apr 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10192 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
CountryJapan
CityKanazawa
Period3/04/175/04/17

Keywords

  • Background subtraction
  • Deep belief network
  • Long tunnel
  • Vehicle detection

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