Support Vector Machines For Understanding Lane Color and Sidewalks
Understanding road features such as lanes, the color
of lanes, and sidewalks in a live video captured from a moving
vehicle is essential to build video-based navigation systems. In this
paper, we present a novel idea to understand the road features using
support vector machines. Various feature vectors including color
components of road markings and the difference between two
regions, i.e., chosen AOIs, and so on are fed into SVM, deciding
colors of lanes and sidewalks robustly. Experimental results are
provided to show the robustness of the proposed idea.
[1] Tanizaki, M., Maruyama, K., Shimada, S., "Acceleration technique of
snake-shaped regions retrieval method for telematics navigation service
system," Proc. Of the International Conference on Data Engineering, 2005,
pp. 949 - 957.
[2] Holzapfel, W., Sofsky, M., Neuschaefer-Rube U., "Road profile
recognition for autonomous car navigation and Navstar GPS support,"
IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, Issue
1, 2003, pp. 2 - 12.
[3] Young Uk Yim and Se-Young Oh, "Three-feature based automatic lane
detection algorithm(TFALDA) for autonomous driving," IEEE
Transactions on ITS, Vol. 4, No 4, 2003, pp. 219 - 225.
[4] Calin Rotaru, Thorsten Graf, and Jianwei Zhang, "Extracting road
features from color images using a cognitive approach," Proc. of IEEE
Intelligent Vehicles Symposium, 2004, pp. 298-303.
[5] K.C. Kluge, C.M. Kreucher, S. Lakshmanan, "Tracking lane and
pavement edges using deformable templates," Proc. SPIE Conference on
Enhanced and Synthetic Vision, 1998, pp. 167-176.
[6] V. Vapnik. Statistical learning theory. John Wiley and Sons, New York,
1998.
[7] Philipp Michel, ana El Kaliouby, "Real time facial expression recognition
in video using support vector machines," Proc. Of 5th international
conference on Multimodal interfaces, 2003, pp.258-264.
[8] M.Pontil, A.Verri,"Support vector machines for 3-d object recognition,"
IEEE Trans. On Pattern Analysis and Machine Intelligene, 1998, pp.
637-646.
[1] Tanizaki, M., Maruyama, K., Shimada, S., "Acceleration technique of
snake-shaped regions retrieval method for telematics navigation service
system," Proc. Of the International Conference on Data Engineering, 2005,
pp. 949 - 957.
[2] Holzapfel, W., Sofsky, M., Neuschaefer-Rube U., "Road profile
recognition for autonomous car navigation and Navstar GPS support,"
IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, Issue
1, 2003, pp. 2 - 12.
[3] Young Uk Yim and Se-Young Oh, "Three-feature based automatic lane
detection algorithm(TFALDA) for autonomous driving," IEEE
Transactions on ITS, Vol. 4, No 4, 2003, pp. 219 - 225.
[4] Calin Rotaru, Thorsten Graf, and Jianwei Zhang, "Extracting road
features from color images using a cognitive approach," Proc. of IEEE
Intelligent Vehicles Symposium, 2004, pp. 298-303.
[5] K.C. Kluge, C.M. Kreucher, S. Lakshmanan, "Tracking lane and
pavement edges using deformable templates," Proc. SPIE Conference on
Enhanced and Synthetic Vision, 1998, pp. 167-176.
[6] V. Vapnik. Statistical learning theory. John Wiley and Sons, New York,
1998.
[7] Philipp Michel, ana El Kaliouby, "Real time facial expression recognition
in video using support vector machines," Proc. Of 5th international
conference on Multimodal interfaces, 2003, pp.258-264.
[8] M.Pontil, A.Verri,"Support vector machines for 3-d object recognition,"
IEEE Trans. On Pattern Analysis and Machine Intelligene, 1998, pp.
637-646.
@article{"International Journal of Information, Control and Computer Sciences:50553", author = "Hoon Lee and Soonyoung Park and Kyoungho Choi", title = "Support Vector Machines For Understanding Lane Color and Sidewalks", abstract = "Understanding road features such as lanes, the color
of lanes, and sidewalks in a live video captured from a moving
vehicle is essential to build video-based navigation systems. In this
paper, we present a novel idea to understand the road features using
support vector machines. Various feature vectors including color
components of road markings and the difference between two
regions, i.e., chosen AOIs, and so on are fed into SVM, deciding
colors of lanes and sidewalks robustly. Experimental results are
provided to show the robustness of the proposed idea.", keywords = "video-based navigation system, lane detection, SVMs, autonomous vehicles", volume = "3", number = "2", pages = "287-4", }