Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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A Method of Lane Marker Detection Robust to Environmental Variation Using Lane Tracking

차선 추적을 이용한 환경변화에 강인한 차선 검출 방법

  • Lee, Jihye (Dept. of Information and Communication Engineering, Graduate School, Handong Global University) ;
  • Yi, Kang (School of Computer Science and Electrical Engineering, Handong Global University)
  • Received : 2018.09.03
  • Accepted : 2018.11.13
  • Published : 2018.12.31
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Abstract

Lane detection is a key function in developing autonomous vehicle technology. In this paper, we propose a lane marker detection algorithm robust to environmental variation targeting low cost embedded computing devices. The proposed algorithm consists of two phases: initialization phase which is slow but has relatively higher accuracy; and the tracking phase which is fast and has the reliable performance in a limited condition. The initialization phase detects lane markers using a set of filters utilizing the various features of lane markers. The tracking phase uses Kalman filter to accelerate the lane marker detection processing. In a tracking phase, we measure the reliability of the detection results and switch it to initialization phase if the confidence level becomes below a threshold. By combining the initialization and tracking phases we achieved high accuracy and acceptable computing speed even under a low cost computing resources in which we cannot use the computing intensive algorithm such as deep learning approach. Experimental results show that the detection accuracy is about 95% on average and the processing speed is about 20 frames per second with Raspberry Pi 3 which is low cost device.

Keywords

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Fig. 1. Conceptual flowchart of the proposed algorithm.

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Fig. 2. Flowchart for initialization of lane marker location.

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Fig. 3. Reference materials of binarization filters.

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Fig. 4. ROI setting and binarization.

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Fig. 5 Explanation materials for characteristics of lane marker.

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Fig. 6. Flowchart for lane marker detection algorithm by tracking.

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Fig. 7. Kalman filter.

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Fig. 8. V-ROI.

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Fig. 9. Shaded image.

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Fig. 10. Detected lane marker of various situation.

Table 1. Accuracy of lane marker detection

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Table 2. Processing time with i7-6700 or Raspberry Pi 3

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Table 3. Processing time(ms) comparison between Raspberry Pi 3 and Odroid-XU4

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References

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