Structured Hough Voting for Vision-based Highway Border Detection

TL;DR

This paper introduces a structured Hough voting method for vision-based highway border detection that leverages geometric relationships and inter-frame tracking to improve robustness and accuracy.

Contribution

It presents a novel joint detection-and-tracking framework using structured Hough voting for highway border detection, enhancing robustness over traditional methods.

Findings

Outperforms baseline methods in qualitative evaluations.

Achieves higher detection accuracy and stability.

Effectively exploits inter-frame structural information.

Abstract

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.