On-Road Vehicle Recognition Using the Symmetry Property and Snake Models

Abstract

Vehicle recognition is a fundamental task for advanced driver assistance systems and contributes to the avoidance of collisions with other vehicles. In recent years, numerous approaches using monocular image analysis have been reported for vehicle detection. These approaches are primarily applied in motorway scenarios and may not be suitable for complex urban traffic with a diversity of obstacles and a clustered background. In this paper, stereovision is firstly used to segment potential vehicles from the traffic background. Given that the contour curve is the most straightforward cue for object recognition, we present here a novel method for complete contour curve extraction using symmetry properties and a snake model. Finally, two shape factors, including the aspect ratio and the area ratio calculated from the contour curve, are used to judge whether the object detected is a vehicle or not. The approach presented here was tested with substantial urban traffic images and the experimental results demonstrated that the correction rate for vehicle recognition reaches 93%.