Road Lane Marker Detection System

Topics: Color, Lane, RGB color model Pages: 5 (1922 words) Published: May 24, 2014

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.

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 information like lane marking, departure and front and side images etc.[2]

Various road conditions make this problem become very challenging including different type of lanes (straight or curvilinear), occlusions caused by obstacles, shadows, lighting changes (like night time), and so on. Lane detection is one important process in the vision based driver assistance system and can be used for vehicle navigation, lateral control, collision prevention, or lane departure warning system.

Many researchers have shown lane detectors based on a wide variety of techniques. Techniques used varied from using monocular [3] to stereo vision [4][5] using low level morphological operations [6][7] to using probabilistic grouping and B-snake [8]. All the techniques are classified into two main categories namely feature based techniques and model based techniques. The feature based technique combines low level features like color; shape etc. in order to detect the lane and the model-based scheme is more robust in lane detection when different lane types with occlusions or shadows are handled. Road and lane markings can vary greatly, making the generation of a single feature-extraction technique is difficult. So, we combined the features of both color based and edge based techniques.

Many lane detection approaches use color model in order to segment the lane line from background images. However, the color feature is not sufficient to decide an exact lane line in images depicting the variety of road markings and conditions. If there are many lanes or obstacle which is similar to lane color, it will be difficult to decide an exact lane. Similarly, some lane detection method uses only edge information. The proposed method involves the combination of both color segmentation and edge orientation to detect lanes of roads of any color (especially yellow and white which are the common colors for the lane). III. COLOR SEGMENTATION

In color segmentation...

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.
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