Multimodal Detection and Tracking of Pedestrians in Urban Environments with Explicit Ground Plane Extraction

This paper presents a novel people detection and tracking method based on a combined multimodal sensor approach that utilizes 2D and 3D laser range and camera data. Laser data points are clustered and classified with a set of geometrical features using an SVM AdaBoost method. The clusters define a region of interest in the image that is adjusted using the ground plane information extracted from the 3D laser. In this areas a novel vision based people detector based on Implicit Shape Model (ISM) is applied. Each detected person is tracked using a greedy data association technique and multiple Extended Kalman Filters that use different motion models. This way, the filter can cope with a variety of different motion patterns. The tracker is asynchronously updated by the detections from the laser and the camera data. Experiments conducted in real-world outdoor scenarios with crowds of pedestrians demonstrate the usefulness of our approach.