Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models

TL;DR

This paper presents a hybrid Bayesian multiobject tracking method that integrates neural networks to enhance traditional models, improving prediction and update steps while maintaining computational efficiency, and achieves state-of-the-art results on autonomous driving data.

Contribution

It introduces a novel hybrid framework combining neural-enhanced statistical models with Bayesian MOT, leveraging belief propagation and Monte Carlo methods for efficient computation.

Findings

Achieves state-of-the-art performance on nuScenes dataset.

Effectively combines model-based and data-driven approaches.

Improves prediction and update accuracy in multiobject tracking.

Abstract

Multiobject tracking (MOT) is an important task in applications including autonomous driving, ocean sciences, and aerospace surveillance. Traditional MOT methods are model-based and combine sequential Bayesian estimation with data association and an object birth model. More recent methods are fully data-driven and rely on the training of neural networks. Both approaches offer distinct advantages in specific settings. In particular, model-based methods are generally applicable across a wide range of scenarios, whereas data-driven MOT achieves superior performance in scenarios where abundant labeled data for training is available. A natural thought is whether a general framework can integrate the two approaches. This paper introduces a hybrid method that utilizes neural networks to enhance specific aspects of the statistical model in Bayesian MOT that have been identified as overly simplistic. By doing so, the performance of the prediction and update steps of Bayesian MOT is improved. To ensure tractable computation, our framework uses belief propagation to avoid high-dimensional operations combined with sequential Monte Carlo methods to perform low-dimensional operations efficiently. The resulting method combines the flexibility and robustness of model-based approaches with the capability to learn complex information from data of neural networks. We evaluate the performance of the proposed method based on the nuScenes autonomous driving dataset and demonstrate that it has state-of-the-art performance.