Road Boundary Detection in Challenging Scenarios

This paper presents a new approach for automatic road detection in traffic cameras. The technique proposed here detects the dominant road boundary and estimates the vanishing point in images captured by traffic cameras under a wide range of lighting and environmental conditions, e.g., in images of unlit highways captured at night, etc. The approach starts by segmenting the traffic scene into a number of superpixel regions. The contours of these regions are used to generate a large number of edges which are organized into clusters of co-linearly similar sets using hierarchical bottom up clustering. A confidence level is assigned to each cluster using a statistical approach and the best clusters are chosen. Pairs of clusters with high confidence levels are then ranked and filtered according to image perspective and activity. The top ranked pair is selected as the road boundary. The proposed technique is tested on a real world dataset collected from the Ontario 401 traffic surveillance system. Experimental results demonstrate a distinct speedup and improvement in accuracy of the proposed technique in detecting the dominant road boundary in challenging scenarios compared to the state of the art Gabor filter based technique.