Multiclass multimodal detection and tracking in urban environments

Abstract

This paper presents a novel approach to detect and track pedestrians and cars based on the combined information retrieved from a camera and a laser range scanner. Laser data points are classified using boosted Conditional Random Fields (CRF), while the image based detector uses an extension of the Implicit Shape Model (ISM), which learns a codebook of local descriptors from a set of handlabeled images and uses them to vote for centers of detected objects. Our extensions to ISM include the learning of object sub-parts and template masks to obtain more distinctive votes for the particular object classes. The detections from both sensors are then fused and the objects are tracked using an Extended Kalman Filter with multiple motion models. Experiments conducted in real-world urban scenarios demonstrate the usefulness of our approach.