论文标题
多对象跟踪中数据关联的神经增强的信念传播
Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking
论文作者
论文摘要
通过多对象跟踪（MOT）方法启用的情况感知技术将在自主导航和应用海科学等领域创建新的服务和应用程序。信仰传播（BP）是贝叶斯MOT的最新方法，但完全依赖于统计模型和预处理的传感器测量。在本文中，我们为基于模型和数据驱动的MOT建立了一种混合方法。提出的神经增强信念传播（NEBP）方法通过从原始传感器数据中学到的信息以改善数据关联并拒绝错误警报测量的目标来补充BP。我们评估了NEBP方法在Nuscenes自动驾驶数据集中的MOT的性能，并证明它可以超越最先进的参考方法。
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.
