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.

翻译：通过多点跟踪(MOT)方法实现的情况认知技术将在自主导航和应用海洋科学等领域创造新的服务和应用。信仰传播(BP)是BayesianMOT最先进的方法,但完全依赖统计模型和预先处理的传感器测量。在本文中,我们为模型和数据驱动的MOT建立了一种混合方法。拟议的神经强化信仰传播(NEBP)方法通过从原始传感器数据获得的信息对BP进行补充,目的是改进数据关联和拒绝虚假的警报测量。我们评估了我们的NEBP方法在nuScenes自主驱动数据集方面的性能,并证明它能够超越最先进的参考方法。