Urban traffic dense-stereo obstacle classification using boosting over visual codebook features

Every driving assistance system should have an obstacle classification module. Its main role is to accurately classify obstacles within a set of predefined classes. This paper presents a real-time dense-stereo based obstacle classification system that integrates visual codebook features like HOG, LBP and texton descriptor types in a powerful classifier. The system classifies the obstacles in four main classes: cars, pedestrians, poles/trees and other obstacles. The system acquires the image scenes using a pair of gray level stereo video-cameras. A combined approach using both 2D intensity and 3D depth information is firstly used for accurate obstacle segmentation. Then, the visual codebook features are extracted for a large set of obstacles with manually labeled classes and used for training a robust boosting classifier. The comparative classification results with an approach based on a random forest classifier trained on a relevant feature set show a considerable improvement, especially for the class of other obstacles.