Support vectors a way to adapt for lane marker tracking: a step towards intelligent transportation systems

The paper describes a novel approach for tracking white lane markers with the view of driving assistance. The presented technique detects the lane markers using a raster scan approach. The detected data points are then converted to functional support vectors using a kernel function derived from the data and are compared with a trained model of similar vectors stored in a d-dimensional tree using a k-nearest neighbor classifier. Experimental results confirm the validity of the presented approach in different lightening conditions and scenarios. The presented technique is capable of detecting vehicles at fourteen frames per sec which makes it ideal for real time pre-crash sensing.