Road Lane Marker Detection System
ROAD LANE DETECTION SYSTEM
Sai Chakradhar Dogiparthi
Abstract - Traffic accidents have become one of the most serious problems in today 's world. Due to day by day increase in population, there are number of vehicles increasing on the roads. As a result, number of accidents is growing day by day. Lane detection is an essential component of Advanced Driver Assistance System. The cognition on the roads is increasing day by day due to increase in the four wheelers on the road. The ignorance towards road rules is contributing to road accidents. The lane marking violence is one of the major causes for accidents on highways. In this work, a robust automatic lane marking detection algorithm is implemented. The HSV color-segmentation based approach is verified for both white lanes and yellow lanes.
I. INTRODUCTION
Traffic accidents have become one of the most serious problems in today 's world. Roads are the choicest and most opted modes of transport in providing the finest connections among all other modes. On average in 2011, 89 people were killed on the roadways of the U.S. each day. From 1979 to 2005, the number of deaths per year decreased 14.97% while the number of deaths per capita decreased by 35.46%. In 2010, there were an estimated 5,419,000 crashes, killing 32,885 and injuring 2,239,000 [1]. The 32,367 traffic fatalities in 2011 were the lowest in 62 years for which statistics are available as shown in Error: Reference source not found.
The major factors that contribute to road accidents are due to negligence of the driver. Reducing the accidents on road is possible by improving the road safety. A real time computer vision based system plays an important role in providing a useful and effective information like lane marking, departure and front and side images etc.
Figure : Fraction of U.S. Motor vehicle deaths relative to total population
A real time computer vision based system plays an important role in providing a useful and effective
Sai Chakradhar Dogiparthi
Abstract - Traffic accidents have become one of the most serious problems in today 's world. Due to day by day increase in population, there are number of vehicles increasing on the roads. As a result, number of accidents is growing day by day. Lane detection is an essential component of Advanced Driver Assistance System. The cognition on the roads is increasing day by day due to increase in the four wheelers on the road. The ignorance towards road rules is contributing to road accidents. The lane marking violence is one of the major causes for accidents on highways. In this work, a robust automatic lane marking detection algorithm is implemented. The HSV color-segmentation based approach is verified for both white lanes and yellow lanes.
I. INTRODUCTION
Traffic accidents have become one of the most serious problems in today 's world. Roads are the choicest and most opted modes of transport in providing the finest connections among all other modes. On average in 2011, 89 people were killed on the roadways of the U.S. each day. From 1979 to 2005, the number of deaths per year decreased 14.97% while the number of deaths per capita decreased by 35.46%. In 2010, there were an estimated 5,419,000 crashes, killing 32,885 and injuring 2,239,000 [1]. The 32,367 traffic fatalities in 2011 were the lowest in 62 years for which statistics are available as shown in Error: Reference source not found.
The major factors that contribute to road accidents are due to negligence of the driver. Reducing the accidents on road is possible by improving the road safety. A real time computer vision based system plays an important role in providing a useful and effective information like lane marking, departure and front and side images etc.
Figure : Fraction of U.S. Motor vehicle deaths relative to total population
A real time computer vision based system plays an important role in providing a useful and effective
References: [2] M. Aly, "Real time detection of lane markings in urban streets," presented at the IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands, 2008. [3] D [4] U. Franke, D. Gavrila, S. Gorzig, F. Lindner, F. Puetzold, andC. Wohler, "Autonomous driving goes downtown", Intelligent Systems and Their Applications, IEEE, 13(6):40–48, Nov.-Dec. 1998. [5] U. Franke and I. Kutzbach, "Fast stereo based object detection for stop and go traffic". In Intelligent Vehicles Symposium, Proceedings of the IEEE, pages 339–344, 19-20 Sept. 1996. [6] M. Bertozzi and A. Broggi, "Real-time lane and obstacle detection on the gold system", Intelligent Vehicles Symposium, Proceedings of the IEEE, pages 213–218, 19-20 Sept. 1996. Conference on, pages 1010–1015, 9-12 Nov. 1997. [8] Yue Wang ,Eam Khwang Teoh, and Dinggang Shen, "Lane detection and tracking using B-Snake", ELSEVIER, Image and Vision Computing 22 (2004) 269–280. [9] Y. He, H. Wang, B. Zhang, Color-Based Road Detection in Urban Traffic Scenes IEEE Transactions on ITS, vol. 5, pp. 309-318 (2004) [10] H.Y [11] Amol Borkar, Monson Hayes and Mark T. Smith, A Template Matching and Ellipse Modeling Approach to Detecting Lane Markers Lecture Notes in Computer Science, 2010, Volume 6475/2010, 179-190, DOI: 10.1007/978-3-642-17691-3_17.