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http://dx.doi.org/10.3745/KTSDE.2015.4.4.201

Detection of Direction Indicators on Road Surfaces Using Inverse Perspective Mapping and NN  

Kim, Jong Bae (서울디지털대학교 컴퓨터정보통신학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.4, 2015 , pp. 201-208 More about this Journal
Abstract
This paper proposes a method for detecting the direction indicator shown in the road surface efficiently from the black box system installed on the vehicle. In the proposed method, the direction indicators are detected by inverse perspective mapping(IPM) and bag of visual features(BOF)-based NN classifier. In order to apply the proposed method to real-time environments, the candidated regions of direction indicator in an image only performs IPM, and BOF-based NN is used for the classification of feature information from direction indicators. The results of applying the proposed method to the road surface direction indicators detection and recognition, the detection accuracy was presented at least about 89%, and the method presents a relatively high detection rate in the various road conditions. Thus it can be seen that the proposed method is applied to safe driving support systems available.
Keywords
Advanced Driver Assistances System(ADAS); Inverse Perspective Mapping(IPM); Bag of Features(BOF);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 J. B. Kim, "Detection of Traffic Signs Based on Eigen-color Model and Saliency Model in Driver Assistance Systems," International Journal of Automotive Technology, Vol.14. No.3, pp.429-439, 2013.   DOI
2 J. C. McCall, M. M. Trivedi, "Video-Based Lane Estimation and Tracking for Driver Assistance: Survey, System, and Evaluation," IEEE Trans. Intell. Transport. Syst., Vol.7, pp. 20-37, 2006.   DOI
3 L. Fletcher, at al., "Driver assistance systems based on vision in and out of vehicles," IEEE Proc. of IVS., pp.322-327, 2003.
4 S. Vacek, C. Schimmel, and R. Dillman, "Road-marking analysis for autonomous vehicle guidance," Proc. of European Conference on Mobile Robots, pp.1-6, 2007.
5 J. P. Gonzalez, U. Ozguner, "Lane Detection Using Histogram-Based Segmentation and Decision Trees," Proc. of IEEE Proc. Intelligent Transportation Systems, pp.346-351, 2000.
6 J. Coughlan, H. Shen, "A fast algorithm for finding crosswalks using figure-ground segmentation," Workshop on Applications of Computer Vision, Vol.5, pp.1-10, 2006.
7 M. Uddin, T. Shioyama. "Robust zebra-crossing detection using bipolarity and projective invariant," Proc. of the Int. Sym. on Signal Processing and Its Applications, Vol.2, pp.517-574, 2005.
8 T. Wu, A. Ranganathan, "A practical system for road marking detection and recognition," IEEE Conf. on IVS., pp.25-30, 2012.
9 Mathworks, http://www.mathworks.com/
10 J. B. Kim, "Detection of direction indictors on road surfaces using Inverse Perspective Mapping and NN," Proceedings of KIPS, Vol.21, No.2, pp.1199-1204, 2014.
11 G. T. Han, H. Hwan, "Multimedia Processing: A Robust Real-Time Lane Detection for Sloping Roads," Journal of KIPS, Vol.2, No.6, pp.413-422, 2013.
12 J. Matas, O. Chum, M. Urba, and T. Pajdla, "Robust wide baseline stereo from maximally stable extremal regions," Pro. of British Machine Vision Conference, pp.384-396, 2002.
13 G. Csurka, et al., "Visual categorization with bags of keypoints," Workshop on Statistical Learning in Computer Vision. ECCV, Vol.1, pp.1-22, 2004.
14 H. Bay, et al., "SURF: Speeded Up Robust Features," "Computer Vision and Image Understanding," CVIU., Vol. 110, No.3, pp.346-359, 2008.
15 I. M. Chira, A. Chibulcutean, and R. G. Danescu, "Real-time detection of road markings for driving assistance applications," IEEE Proc. of Computer Engineering and Systems, pp.158-163, 2010.