Motion Estimation Aided Detection of Roadside Vegetation

In this paper we present a method for roadside vegetation detection from video obtained from a moving vehicle with intended use in road infrastructure maintenance and traffic safety. While many published methods are using Near Infrared images which are suitable for vegetation detection, our method uses image features from the visible spectrum allowing the use of a common color camera. The presented detection method uses a set of carefully selected color and texture features. Texture features are based on two-dimensional Continuous Wavelet Transform with oriented wavelets. As selected features can vary with the distance from the camera, we are limiting detection to the regions near to the camera. We used an optical flow algorithm as an approximate estimator of the distance. The classification into vegetation and non-vegetation regions was done using nonlinear SVM. For training and testing purposes we recorded our own video database which contains roadside vegetation in various conditions. We are presenting promising experimental results, comparison with an alternative approach and a discussion of specific problems experienced or expected in real-world application of the method.