In this paper, we present improved lane tracking using vehicle localization. Lane markers are detected using a bank of steerable filters, and lanes are tracked using Kalman filtering. On-road vehicle detection has been achieved using an active learning approach, and vehicles are tracked using a Condensation particle filter. While most state-of-the art lane tracking systems are not capable of performing in high-density traffic scenes, the proposed framework exploits robust vehicle tracking to allow for improved lane tracking in high density traffic. Experimental results demonstrate that lane tracking performance, robustness, and temporal response are significantly improved in the proposed framework, while also tracking vehicles, with minimal additional hardware requirements.
Improved vision-based lane tracker performance using vehicle localization
2010 IEEE Intelligent Vehicles Symposium ; 676-681
2010-06-01
634187 byte
Conference paper
Electronic Resource
English
Improved Vision-Based Lane Tracker Performance Using Vehicle Localization, pp. 676-681
British Library Conference Proceedings | 2010
|Performance Evaluation of a Vision Based Lane Tracker Designed for Driver Assistance Systems
British Library Conference Proceedings | 2005
|Vision-based approach towards lane line detection and vehicle localization
British Library Online Contents | 2016
|