Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real time facial expression recognition in video using support vector machines
Proceedings of the 5th international conference on Multimodal interfaces
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving
IEEE Transactions on Intelligent Transportation Systems
Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation
IEEE Transactions on Intelligent Transportation Systems
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Understanding lane is an essential step to provide more realistic information for video-based navigation systems. In this paper, we present a novel idea to understand lane from a live video captured in a moving vehicle. More specifically, 1) lane markings are extracted first. Then, 2) color information of lane markings are fed into support vector machines to decide if it is yellow lane or not. By combining information from database, it is possible to decide if we are in the leftmost, middle, or the rightmost lane, which allows us to provide more realistic navigation information to drivers. Exhaustive simulation results are provided to show the robustness of the proposed idea.