Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking
This work addresses multiobject tracking for applications like autonomous navigation, representing an incremental improvement by enhancing an existing method with learned information.
The paper tackled the problem of multiobject tracking by proposing a hybrid method that combines belief propagation with neural networks to improve data association and reject false alarms, demonstrating performance that outperforms state-of-the-art methods on the nuScenes dataset.
Situation-aware technologies enabled by multiobject tracking (MOT) methods will create new services and applications in fields such as autonomous navigation and applied ocean sciences. Belief propagation (BP) is a state-of-the-art method for Bayesian MOT but fully relies on a statistical model and preprocessed sensor measurements. In this paper, we establish a hybrid method for model-based and data-driven MOT. The proposed neural enhanced belief propagation (NEBP) approach complements BP by information learned from raw sensor data with the goal to improve data association and to reject false alarm measurements. We evaluate the performance of our NEBP approach for MOT on the nuScenes autonomous driving dataset and demonstrate that it can outperform state-of-the-art reference methods.