Robust Vehicle Tracking with Monocular Vision Using Convolutional Neuronal Networks

In this paper we present a robust tracking system that enables an autonomous vehicle to follow a specific convoy leader. Images from a single camera are used as input data, from which predefined keypoints on the lead vehicle are detected by a convolutional neural network. This approach was inspired by the idea of human pose estimation and is shown to be significantly more accurate compared to standard bounding box detection approaches like YOLO.The estimation of the dynamic state of the leading vehicle is realized by means of a moving horizon estimator. We show the practical capabilities and usefulness of the system in real-world experiments. The experiments show that the tracking system, although it only operates with images, is competitive with earlier approaches that also used other sensors such as LiDAR.