Recursive Video Lane Detection

TL;DR

This paper introduces RVLD, a recursive video lane detection algorithm that combines intra-frame detection with predictive modeling using motion estimation to improve lane detection in videos.

Contribution

The paper presents a novel recursive approach combining intra-frame and predictive detection for improved video lane detection performance.

Findings

Outperforms existing video lane detectors on benchmark datasets.

Effectively utilizes previous frame information for more reliable detection.

Demonstrates robustness in various video conditions.

Abstract

A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively to the next frame. RVLD consists of an intra-frame lane detector (ILD) and a predictive lane detector (PLD). First, we design ILD to localize lanes in a still frame. Second, we develop PLD to exploit the information of the previous frame for lane detection in a current frame. To this end, we estimate a motion field and warp the previous output to the current frame. Using the warped information, we refine the feature map of the current frame to detect lanes more reliably. Experimental results show that RVLD outperforms existing detectors on video lane datasets. Our codes are available at https://github.com/dongkwonjin/RVLD.