Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking

TL;DR

This paper introduces NEBP, a hybrid neural and belief propagation method that enhances multi-object tracking by learning from raw sensor data, improving data association and false alarm rejection in autonomous driving scenarios.

Contribution

The paper presents a novel neural enhanced belief propagation approach that combines model-based and data-driven techniques for improved multi-object tracking.

Findings

NEBP outperforms state-of-the-art methods on nuScenes dataset.

It improves data association accuracy and false alarm rejection.

Demonstrates effectiveness in autonomous driving applications.

Abstract

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.