dongkwonjin/Eigenlanes

Official code for "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes", CVPR2022

42
/ 100
Emerging

This project helps self-driving car engineers and researchers accurately identify lane markings in diverse and complex driving scenarios. It takes raw image or video data of road scenes and outputs highly precise lane line detections, even when lanes are structurally varied or obscured. Autonomous vehicle perception system developers would use this to improve the robustness and reliability of their lane-keeping and navigation systems.

135 stars. No commits in the last 6 months.

Use this if you need a cutting-edge method for detecting and describing lane structures in real-world driving footage, especially when traditional methods struggle with complex lane layouts.

Not ideal if you are looking for a plug-and-play solution without any technical setup, as it requires familiarity with deep learning frameworks and command-line operations.

autonomous-driving lane-detection computer-vision vehicle-perception road-scene-analysis
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 16 / 25

How are scores calculated?

Stars

135

Forks

18

Language

Python

License

Apache-2.0

Last pushed

Jul 21, 2022

Commits (30d)

0

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