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http://dx.doi.org/10.5762/KAIS.2012.13.12.6089

Method of Tunnel Incidents Detection Using Background Image  

Jeong, Sung-Hwan (Korea Electronics Technology Institute)
Ju, Young-Ho (Dept. of Computer Engineering, Chonbuk National University)
Lee, Jong-Tae (Corp. Micronet)
Lee, Joon-Whoan (Dept. of Computer Engineering, Chonbuk National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.13, no.12, 2012 , pp. 6089-6097 More about this Journal
Abstract
This study suggested a method of detecting an incident inside tunnel by using camera that is installed within the tunnel. As for the proposed incident detection method, a static object, travel except vehicles, smoke, and contra-flow were detected by extracting the moving object through using the real-time background image differencing after receiving image from the camera, which is installed inside the tunnel. To detect the moving object within the tunnel, the positive background image was created by using the moving information of the object. The incident detection method was developed, which is strong in a change of lighting that occurs within the tunnel, and in influence of the external lighting that occurs in the entrance and exit of the tunnel. To examine the efficiency of the suggested method, the experimental images were acquired from Marae tunnel and Expo tunnel in Yeosu of Jeonnam and from Unam tunnel in Imsil of Jeonbuk. Number of images, which were used in experiment, included 20 cases for static object, 20 cases for travel except vehicles, 4 cases for smoke, and 10 cases for contra-flow. As for the detection rate, all of the static object, the travel except vehicles, and the contra-flow were detected in the experimental image. In case of smoke, 3 cases were detected. Thus, excellent performance could be confirmed. The proposed method is now under operation in Marae tunnel and Expo tunnel in Yeosu of Jeonnam and in Unam tunnel in Imsil of Jeonbuk. To examine accurate efficiency, the evaluation of performance is considered to be likely to be needed after acquiring the incident videos, which actually occur within tunnel.
Keywords
Tunnel Incident Detection; Incident Detection; Traffic Surveillance; Accident Detection in Tunnel; Smoke Detection;
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1 HeeSin Lee, SungHwan Jeong, Joonwhoan Lee, "Vision-Based Fase Detection System for Tunnel Incidens", Korea ITS Journal, Vol. 9, No. 1, pp. 9-18, 2010.
2 Shunsuke Kamijo, Hiroshi Inoue, "Incident Detection from Low-angle Images of Heavy Traggic in Tunnels", IEEE Intelligent Transportation Systems Conference, pp. 81-86, 2007, Article(CrossRefLink)
3 BingFei Wu, ChihChung Kao, ChihChun Liu, ChungJui Fan, ChaoJung Chen, "The Vision-based Vehicle Detection and Incident Detection System in Hsueh-Shan Tunnel", IEEE International Symposium Industrial Electronics, pp. 1394-1399, 2008.
4 DarShyang Lee, "Effective Gaussian Mixture Learning for Video Background Subtraction", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, pp. 827-832, May 2005, Article(CrossRefLink)   DOI   ScienceOn
5 SungHwan Jeong, Joonwhoan Lee, "Non-Parametric Background Image Generation Based on Moving Objects", IEEK Conference, Vol. 34, No. 1, pp. 765-768, 2011.
6 SungHwan Jeong, HeeSin Lee, Joonwhoan Lee, "Detection Method of Accident and Parking Violation for Uninterrupted Flow Facility," Korea ITS Conference, pp. 205-208, 2009.
7 Du-Ming Tsai, Chien-Ta Lin, Jeng-Fung Chen, "The evaluation of normalized cross correlations for defect detection", Pattern Recognition Letters, Vol. 24, pp. 2525-2535, 2003, Article(CrossRefLink)   DOI
8 Du-Ming Tsai, Chien-Ta Lin, "Fast normalized cross correlation for defect detection", Pattern Recognition Letters, Vol. 24, pp. 2625-2631, 2003, Article(CrossRefLink)   DOI
9 Timo Ojala, Matti Pietikainen, Maenpaa, "Multisolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, 2002, Article(CrossRefLink)
10 Yu Cui, Hua Dong, Enze Zhou, "An Early Fire Detection Method Based on Smoke Texture Analysis and Discrimination", IEEE Congress on Image and Signal Processing, pp. 95-99, 2008, Article(CrossRefLink)
11 Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing - Second Edition," Prentice Hall, pp. 534-550, 2002.
12 SungHwan Jeong, Joonwhoan Lee, "Measurement of the Traffic Congestion using Difference of Images in Intersection", IEEK Conference, pp. 801-802, 2008.