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Journal of Information Science and Engineering, Vol. 27 No. 2, pp. 715-732 (March 2011)

Jointing Edge Labeling and Geometrical Constraint for Lane Detection and its Application to Suspicious Driving Behavior Analysis*

1Department of Electrical Engineering
Yuan Ze University
Chungli, 320 Taiwan
2Department of Computer Science and Engineering
National Taiwan Ocean University
Keelung, 202 Taiwan

This paper presents a novel lane detection scheme for detecting and tracking various lanes directly from videos in real time. The key contribution of this paper is to propose a new edge labeling scheme for labeling each edge point to different types. Even under different lighting and weather conditions, different lane segments still can be effectively extracted from the labeling result. Only the subtraction and counting operators are involved in the labeling process. It is also flexible for detecting lanes with different types especially from an interchange area. To filter out false lane segments, this paper uses a pinhole camera model to derive a geometrical constraint for lane verification. The constraint is invariant to shadows and lighting changes. Thus, each lane line can be verified and then detected more accurately from roads. Since lanes seldom change their colors during two adjacent frames, we propose a kernel-based technique for tracking them even fragmented into pieces of segments. Then, different lanes can be more efficiently detected and tracked from videos captured under various lighting and weather conditions. With the lane information, different dangerous driving events like lane departure can be easily analyzed for driver assistances. All the involved operations are very simple and effective for hardware implementation. Extensive experimental results reveal the feasibility and superiority of the proposed approach in lane detection.

Keywords: lane detection, edge labeling, lane departure, suspicious event analysis, curve fitting

Full Text () Retrieve PDF document (201103_19.pdf)

Received April 14, 2009; revised October 19, 2009 & April 15, 2010; accepted May 24, 2010.
Communicated by Tong-Yee Lee.
* This work was supported in part by the National Science Council of Taiwan, R.O.C. under Grant No. NSC 97- 2221-E-155-036, and the Ministry of Economic Affairs under contract No. 96-EC-17-A-02-S1-032 and the "Digital Life Sensing and Recognition Application Technologies Project" of the Institute for Information Industry.