Lane markers detection

Lane markers detection

Michal Polko

In this project, we detect lane markers in videos taken with dashboard camera.


  1. Convert a video frame to grayscale, boost contrast and apply dilation operator to highlight lane markers in the frame.

    Highlighted lane markers.

    cvtColor(frame, frame_bw, CV_RGB2GRAY);
    frame_bw.convertTo(frame_bw, CV_32F, 1.0 / 255.0);
    pow(frame_bw, 3.0, frame_bw);
    frame_bw *= 3.0;
    frame_bw.convertTo(frame_bw, CV_8U, 255.0);
    dilate(frame_bw, frame_bw, getStructuringElement(CV_SHAPE_RECT, Size(3, 3)));
  2. Apply the Canny edge detection to find edges.

    Application of the Canny edge detection.

    int cny_threshold = 100;
    Canny(frame_bw, frame_edges, cny_threshold, cny_threshold * 3, 3);
  3. Apply the Hough transform to find line segments.
    vector<Vec4i> hg_lines;
    HoughLinesP(frame_edges, hg_lines, 1, CV_PI / 180, 15, 15, 2);
  4. Since the Hough transform returns all line segments, not only those around lane markers, it is necessary to filter the results.
    1. We create two lines that describe boundaries of the current lane (hypothesis).
      1. We place two converging lines in the frame.
      2. Using brute-force search, we try to find position where they capture as many line segments as possible.
      3. Since road in the frame can have more than one lane, we try to find result as narrow as possible.
    2. We select line segments that are captured by the created hypothesis, mark them as lane markers and draw them.
    3. Each frame, we take the detected lane markers from the previous frame and perform linear regression to adjust the hypothesis (continuous adjustment).
    4. If we cannot find lane markers in more than 5 successive frames (due to failure of continuous adjustment, lane change, intersection, …), we create a new hypothesis.
    5. If the hypothesis is too wide (almost full width of the frame), we create a new one, because arrangement of road lanes might have changed (e.g. additional lane on freeway).
  5. To distinguish between solid and dashed lane markers, we calculate coverage of the hypothesis by line segments. If the coverage is less than 60%, it is a dashed line; if more, it is a solid line.


    Filtered result of the Hough transform + detection of solid/dashed lines.