1 research outputs found
Structured Hough Voting for Vision-based Highway Border Detection
We propose a vision-based highway border detection algorithm using structured
Hough voting. Our approach takes advantage of the geometric relationship
between highway road borders and highway lane markings. It uses a strategy
where a number of trained road border and lane marking detectors are triggered,
followed by Hough voting to generate corresponding detection of the border and
lane marking. Since the initially triggered detectors usually result in large
number of positives, conventional frame-wise Hough voting is not able to always
generate robust border and lane marking results. Therefore, we formulate this
problem as a joint detection-and-tracking problem under the structured Hough
voting model, where tracking refers to exploiting inter-frame structural
information to stabilize the detection results. Both qualitative and
quantitative evaluations show the superiority of the proposed structured Hough
voting model over a number of baseline methods