We try to detect lane lines on a running track (and with this information the upcoming direction).
We currently use the following (simplified) steps:
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Binary: transform the input with a binary threshold
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Cropped: crop the region of interest (currently just the lower half of the image)
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Canny: detect edges and group them with HoughLinesP
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Upward Lines & Closing: only keep lines with an extreme slope - ignore horizontal lines
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Result: find connected components and fit quadratic function through each line
This works in general (examples: straight.png, left.png) but has issues if for example a horizontal connection line wasn't removed (example: problems.png - bottom right & bottom left). In such a case the two lines and the connection get interpreted as one connected component.
As our point of view can tilt a lot from left to right (camera mounted on a running person) it is quite hard to define a slope threshold for upwards pointing lines.
Is there a better way to get rid of non upwards pointing track lines? As the current solution with canny, hough transform and slope filtering is not optimal for curves and sometimes doesn't work at all (as described above).
Would it be possible to get from the cropped image straight to the separated lines through morphology operations? Similar to this example. I know we have no strict horizontal lines which makes any fitting kernel a lot more complex (I assume).
Currently, we try to use perspective transformations to get a bird view on the track. This should help to distinguish between horizontal and upward pointing lines.
Another small issue is too short lines which result in inaccurate approximated quadratic functions (most left line in problems.png and straight.png). This might be easily solvable (by requiring a minimal pixel count for a component to be counted as track line) and should not be part of this question.
Straight
Left
Problems
from Extract upwards pointing lane lines
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