Browse > Article
http://dx.doi.org/10.9708/jksci.2017.22.03.027

Lane Detection Using Biased Discriminant Analysis  

Kim, Tae Kyung (Dept. of Computer Science and Engineering, Dankook University)
Kwak, Nojun (Dept. of Transdisciplinary Studies, Seoul National University)
Choi, Sang-Il (Dept. of Computer Science and Engineering, Dankook University)
Abstract
We propose a cascade lane detector that uses biased discriminant analysis (BDA) to work effectively even when there are various external factors on the road. The proposed cascade detector was designed with an existing lane detector and the detection module using BDA. By placing the BDA module in the latter stage of the cascade detector, the erroneously detected results by the existing detector due to sunlight or road fraction were filtered out at the final lane detection results. Experimental results on road images taken under various environmental conditions showed that the proposed method gave more robust lane detection results than conventional methods alone.
Keywords
Lane detection; Advanced driver assistance systems; Biased discriminant analysis;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 K. Matheus and T. Konigseder, "Automotive ethernet," Cambridge University Press, Cambridge, England, 2014.
2 D. Felguera-Martin and J. T. Gonzalez-Partida, P. Almorox-Gonzalez, and M. Burgos-Garcia, "Vehicular traffic surveillance and road lane detection using radar interferometry," IEEE transactions on vehicular technology, Vol. 61, No. 3, pp. 959-970, Jan., 2012.   DOI
3 J. Sparbert, K. Dietmayer, and D. Streller, "Lane detection and street type classification using laser range images," IEEE Transactions on Intelligent Transportation Systems, pp. 454-459, Aug., 2001.
4 S. Nedevschi, R. Schmidt, T. Graf, R. Danescu, D. Frentiu, T. Marita, and C. Pocol, "3D lane detection system based on stereovision," IEEE Transactions on Intelligent Transportation Systems, pp. 161-166, Oct., 2004.
5 S. D. Min, and C. K Kwon, "Step Counts and Posture Monitoring System using Insole type Textile Capacitive pressure Sensor For Smart Gait Analysis," Journal of The Korea Society of Computer and Information, Vol. 17, No. 8, pp. 107-114, Aug., 2012.   DOI
6 Li, Q., Chen, L., Li, M., Shaw, S. L., and Nuchter, A. "A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios," IEEE Transactions on Vehicular Technology, Vol. 63, No. 2, pp. 540-555, Feb., 2014.   DOI
7 J. Y. Sung, M. H. Han, and K. H. Ro,"Development of a Vision-based Lane Change Assistance System for Safe Driving." Journal of The Korea Society of Computer and Information, Vol. 11, No. 5, pp. 329-336, Nov., 2006.
8 A. Bar Hillel, R. Lerner, D. Levi, and G. Raz, "Recent progress in road and lane detection: a survey." Machine vision and applications, pp. 1-19, April, 2014.
9 J. Son, H. Yoo, S. Kim, and K. Sohn, "Real-time illumination invariant lane detection for lane departure warning system," Expert Systems with Applications, Vol. 42, No. 4, pp. 1816-1824, March, 2015.   DOI
10 H. Yoo, U. Yang, and K. Sohn, "Gradient-enhancing conversion for illumination-robust lane detection." IEEE Transactions on Intelligent Transportation Systems, Vol. 14, No. 3, pp. 1083-1094, Sep, 2013.   DOI
11 K. Y. Chiu, and S. F. Lin, "Lane detection using color-based segmentation", In Intelligent Vehicles Symposium, pp. 706-711, 2005.
12 D. Schreiber, D. B. Alefs, and M. Clabian, "Single camera lane detection and tracking," IEEE Transactions on Intelligent Transportation Systems, pp. 302-307, Sep., 2005.
13 M. Aly, "Real time detection of lane markers in urban streets," In Intelligent Vehicles Symposium, pp. 7-12, 2008.
14 J. Canny, "A computational approach to edge detection," IEEE Transactions on pattern analysis and machine intelligence, Vol. 8, No. 6, pp. 679-698, Nov., 1986.
15 M. Turk, and A. Pentland, "Eigenfaces for recognition," Journal of cognitive neuroscience, Vol. 3, No. 1, pp. 71-86, Dec., 1991.   DOI
16 A. H. Lai, and N. H. Yung, "Lane detection by orientation and length discrimination," IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 30, No. 4, pp. 539-548, Aug., 2000.   DOI
17 R. Gopalan, T. Hong, M. Shneier, and R. Chellappa, "Video-based lane detection using boosting principles," Snowbird Learning, 2009.
18 R. Gopalan, T. Hong, M. Shneier, and R. Chellappa, "A learning approach towards detection and tracking of lane markings," IEEE Transactions on Intelligent Transportation Systems, Vol. 13, No. 3, pp. 1088-1098, Sep., 2012.   DOI
19 R. E. Schapire, and Y. Singer, "Improved boosting algorithms using confidence-rated predictions," Machine learning, Vol. 37, No. 3, pp. 297-336, Dec., 1999.   DOI
20 H. A. Mallot, H. H. Bulthoff, J. J. Little, and S. Bohrer, "Inverse perspective mapping simplifies optical flow computation and obstacle detection," Biological cybernetics, Vol. 64, No. 3, pp. 177-185, Jan., 1991.   DOI
21 M. A. Fischler, and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, Vol. 24, No. 6, pp. 381-395, June, 1981.   DOI
22 P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on pattern analysis and machine intelligence, Vol. 19, No. 7, pp. 711-720, July, 1997.   DOI
23 X. S. Zhou, and T. S. Huang, "Small sample learning during multimedia retrieval using biasmap," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 11-17, Dec., 2001.
24 Y. Lee, and S. I. Choi, "A New Confidence Measure for Eye Detection Using Pixel Selection," Journal of Korea Information Processing Society, Vol. 4, No. 7, pp. 291-296, July, 2015.