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Situation-aware technologies enabled by multiobject tracking (MOT) methodswill create new services and applications in fields such as autonomousnavigation and applied ocean sciences. Belief propagation (BP) is astate-of-the-art method for Bayesian MOT but fully relies on a statisticalmodel and preprocessed sensor measurements. In this paper, we establish ahybrid method for model-based and data-driven MOT. The proposed neural enhancedbelief propagation (NEBP) approach complements BP by information learned fromraw sensor data with the goal to improve data association and to reject falsealarm measurements. We evaluate the performance of our NEBP approach for MOT onthe nuScenes autonomous driving dataset and demonstrate that it can outperformstate-of-the-art reference methods.

Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking