Towards data-driven multi-target tracking for autonomous driving

Christian Fruhwirth-Reisinger, Georg Krispel, Horst Possegger, Horst Bischof

Abstract: We investigate the potential of recurrent neural networks (RNNs) to improve traditional online multi-target tracking of traffic participants from an ego-vehicle perspective. To this end, we build a modular tracking framework, based on interacting multiple models (IMM) and unscented Kalman filters (UKF). Following the tracking-by-detection paradigm, we leverage geometric target properties provided by publicly available 3D object detectors. We then train and integrate two RNNs: A state prediction network replaces hand-crafted motion models in our filters and a data association network finds detection-to-track assignment probabilities. In our extensive evaluation on the publicly available KITTI dataset we show that our trained models achieve competitive results and are significantly more robust in the case of unreliable object detections.