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http://dx.doi.org/10.6109/jkiice.2014.18.8.1885

Automatic Detection of Vehicle Area Rectangle and Traffic Volume Measurement through Vehicle Sub-Shadow Accumulation  

Kim, Jee-Wan (Department of Electronic Engineering, Korea National University of Transportation)
Lee, Jaesung (Department of Electronic Engineering, Korea National University of Transportation)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.18, no.8, 2014 , pp. 1885-1894 More about this Journal
Abstract
There are various high-performance algorithms in the area of the existing VDSs (vehicle detection systems). However, they requires a large amount of computational time-complexity and their systems generally are very expensive and consumes high-power. This paper proposes real-time traffic information detection algorithm that can be applied to low-cost, low-power, and open development platform such as Android. This algorithm uses a vehicle's sub-shadow to set ROI(region of interest) and to count vehicles using a location of the sub-shadow and the vehicle. The proposed algorithm is able to count the vehicles per each roads and each directions separately. The experiment result show that the detection rate for going-up vehicles is 94.1% and that for going-down vehicles is 97.1%. These results are close to or surpasses 95%, the detection rate of commercial loop detectors.
Keywords
VDS; sub-shadow; time complexity; Android; traffic volume;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Lowe, David G., "Object recognition from local scaleinvariant features", Proceedings of the International Conference on Computer Vision, pp. 1150-1157. 1999.
2 Suk-Tae Kim et. al., "The Next Generation Smart Vehicle Detection Technology, SMART-I", The Road Traffic, pp 26-33, no. 129, 2012.
3 Suk-il Song, Jaesung Lee, Gyoon-Byung Ko, and Chul Moon, "Research Trend on Elementary Technologies of the Next Generation Smart Transportation System", Information and Communications Magazine, pp. 18-pp. 24, vol. 30, no. 10, October 2013.
4 Barron, J.L., D.J. Fleet, S.S. Beauchemin, and T.A. Burkitt. Performance of optical flow techniques. CVPR, 1992.
5 http://www.inpai.com.cn/doc/hard/198143_-3.htm
6 Tekalp, A. M., Smolic, A., Vetro, A., and Onural, L., Eds. "Tracking and counting vehicles in traffic video sequences using particle filtering ," vol. 99, 4. Proceedings of the IEEE. 2011.
7 In Jung Lee, Joon Young Min, Hyoung Lee, "An Algorithm for Analysing Occulsion using ICA Method", Proceedings of KITA conference pp.457-466, 2004.
8 Kim, Giseok, and Jae-Soo Cho. "Vision-based vehicle detection and inter-vehicle distance estimation." Control, Automation and Systems (ICCAS), 2012 12th International Conference on. IEEE, 2012.
9 Jun-Hee Cho, Hee-Sung Kim, "Counting the number of cars waiting at the traffic light by computer vision process" Proc. of the 30th KIISE Fall Conference, Vol. no.2, pp.562-564, Oct. 2003.   과학기술학회마을
10 Piccardi, Massimo. "Background subtraction techniques: a review." Systems, man and cybernetics, 2004 IEEE international conference on. Vol. 4. 2004.
11 Bong-Keun Kim, "A Study of Non-ROI Real-time CCTV Visibility Measurements for Highway Fog Warning System." Journal of the Korea Academia-Industrial cooperation Society conference, 709-712 , 2009 .   과학기술학회마을