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http://dx.doi.org/10.3745/KTSDE.2015.4.2.83

An In-Tunnel Traffic Accident Detection Algorithm using CCTV Image Processing  

Baek, JungHee (숭실대학교 미디어학과)
Min, Joonyoung (상지영서대학교 국방정보통신과)
Namkoong, Seong (한국도로공사 도로교통연구원 교통연구실)
Yoon, SeokHwan (세명대학교 컴퓨터학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.2, 2015 , pp. 83-90 More about this Journal
Abstract
Almost of current Automatic Incident Detection(AID) algorithms involve the vulnerability that detects the traffic accident in open road or in tunnel as the traffic jam not as the traffic accident. This paper proposes the improved accident detection algorithm to enhance the detection probability based on accident detection algorithms applied in open roads. The improved accident detection algorithm provides the preliminary judgment of potential accident by detecting the stopped object by Gaussian Mixture Model. Afterwards, it measures the detection area is divided into blocks so that the occupancy rate can be determined for each block. All experimental results of applying the new algorithm on a real incident was detected image without error.
Keywords
CCTV Image Processing; In-tunnel Accident Detection; Automatic Incident Detection Algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 C. Naussbaumer, "Comparative analysis of safety in tunnels," in Young Reseachers Seminar 2007, European Conference of Transport Research Institutes, 2007.
2 F. Andres, O. N. Jorge, J. Vedran, P. Aleksandra and P. Wilfried, "A Mathematical Morphology based Approach for Vehicle Detection in Road Tunnels," in Proceedings of SPIE, the International Society for Optical Engineering, Vol.8135, 2011.
3 e-Narajipyo, Statistics of Road bridges and tunnels in Korea[internet], http://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=1213.
4 H. T. Kim, G. H. Lee, J. S. Park, and Y. S. Yu, "Vehicle Detection in Tunnel using Gaussian Mixture Model and Mathematical Morphological Processing," Journal of the Korea Institute of Electronic Communication Sciences, Vol.7, No.5, pp.967-974, 2012.   DOI
5 B. Martin, S. Vogler, C. Diers, M. Martens, J. Lacroix, M. Steiner, P. Schmitz, and M. Serrano, "Recommendations for the enhancement of Preventive Tunnel safety," SafeT Work package 2 Final Report, 2005.
6 Ministry of Land, Infrastructure and Transport, "The National Guideline for the Installation and Management of Road Tunnel Fire Safety Facilities," Seoul: Ministry of Land, Infrastructure and Transport, 2009.
7 J. Oh, J. Y. Min, "Gaussian background mixture model based automatic incident detection system for real-time tracking," Canadian Journal of Civil Engineering, Vol.38, pp.1158-1169, 2011.   DOI
8 H. Schwabach, M. Harrer, W. Holzmann, H. Bischof, G. Fernandez Dominguez, M. Noolle, R. Pflugfelder, B. Strobl, A. Tacke, and A. Waltl, "VIDEO BASED IMAGE ANALYSIS FOR TUNNEL SAFETY-VITUS-1: A TUNNEL VIDEO SURVEILLANCE AND TRAFFIC CONTROL SYSTEM," in Proceedings of the 12th World Congress on Intelligent Transport Systems, 2005.
9 Korea Expressway Cooperation, "A Study on Constructing Evaluation method of Video Incident Detection system in Freeway Tunnels and Extended application plans," Vol.1-Vol.4, 2012.
10 J. Versavel, B. Boucke, "Operational Traffic Management by Using Video Detection," ITS America. Meeting (8th: 1998: Detroit, Mich.), Transportation technology for tomorrow: conference proceedings. 1998.
11 B. Strobl, M. Harrer, G. Zoffmann, H. Bischof, A. Tacke, A. Waltl, C. Beleznai, M. Dittrich, H. Grabner, H. Schwabach, and G. Fernadez Dominguez, "VITUS-TUNNEL SAFETY THROUGH VIDEO BASED IMAGE ANALYSIS," Proceedings Of The 13th ITS WORLD CONGRESS, London, 8-12th, Oct., 2006.
12 H. Rakha, B. Hellinga, and M. V. Aerde, "Testbed for Evaluating Automatic Incident Detection Algorithms," in Intelligent Transportation System Safety and Security Conference, Miami, 24-25th Mar., 2004.
13 J. J. Reijmers, Traffic Guidance System[internet] http://www.pn.ewi.tudelft.nl/education/et-024/notes/h12.pdf
14 C. Stauffer, W. Grimson, "Adaptive background mixture models for real-time tracking," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.246-252, 1999.