Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models

2025-06-22

AI Summary

In Bayesian multi-object tracking, model-driven approaches exhibit strong generalization but limited representational capacity, whereas data-driven methods achieve superior performance yet suffer from distributional dependence. To address this trade-off, we propose a hybrid framework that synergistically integrates model-based and data-driven paradigms. Grounded in Bayesian filtering, the framework employs neural networks to adaptively refine motion and observation models—thereby enhancing modeling accuracy while preserving physical interpretability. Furthermore, it combines belief propagation with sequential Monte Carlo inference to enable efficient, low-dimensional reasoning in high-dimensional state spaces. Evaluated on the nuScenes dataset, our method achieves state-of-the-art performance: +3.2% improvement in MOTA and +4.7% in IDF1, demonstrating superior tracking accuracy and trajectory continuity. The approach exhibits both robustness to distributional shifts and strong generalization across diverse scenarios.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Bayesian Learning
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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

Problem

• Integrates model-based and data-driven multiobject tracking methods
• Enhances Bayesian MOT with neural networks for better performance
• Ensures tractable computation via belief propagation and Monte Carlo

Innovation

• Hybrid Bayesian MOT with neural-enhanced models
• Belief propagation for tractable computation
• Combines model-based and data-driven advantages