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
01.06.2010
634187 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Improved Vision-Based Lane Tracker Performance Using Vehicle Localization, pp. 676-681
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