A trained system for multimodal perception in urban environments
Luciano Spinello, Rudolph Triebel · 2009
Abstract—This paper presents a novel approach to detect and track multiple classes of objects based on the combined information retrieved from camera and laser rangescanner. Laser data points are classified using Conditional Random Fields (CRF) that use a set of multiclass Adaboost classified features. The image detection system is based on Implicit Shape Model (ISM) that learns an appearance codebook of local descriptorsfromasetofhand-labeledimagesofpedestriansand uses them in a voting scheme to vote for centers of detected people. We propose several extensions in the training phase in order to automatically create subparts and probabilistic shape templates, and in the testing phase in order to use these extended information to select and discriminate between hypothesis of different classes. Finally the two information are combined during tracking that is based on kalman filters with multiple motion models. Experiments conducted in real-world urban scenarios demonstrate the usefulness of our approach. I.