Real-time unsupervised feature model generation for a vehicle following system

This paper describes a real-time method for building 3D feature models of an object of interest, e.g. a vehicle. The model generation works unsupervised and consists of four recursive steps. The first two steps identify the object of interest and extract the object-related sensor data. In the third and fourth step, prominent features are detected and integrated into a common 3D feature model. This method is primarily designed for a model-based vehicle following system. The system requires a model with prominent features for vehicle tracking by day and night. We present an unsupervised algorithm that processes data from different sensor types to extract the object's prominent features over time. Common sensor types are LiDAR, color- and thermal cameras. We generated feature models of different vehicles online and tested them with a vehicle following system afterwards. Using these generated models, the model-based vehicle detection of the following system could detect convoy leaders with high accuracy and without interruptions.