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.

翻译：多目标跟踪（MOT）是自动驾驶、海洋科学和航空航天监视等应用中的重要任务。传统的MOT方法是基于模型的，它将序贯贝叶斯估计与数据关联以及目标新生模型相结合。更近期的方法是完全数据驱动的，依赖于神经网络的训练。这两种方法在特定场景下各具优势。具体而言，基于模型的方法通常适用于广泛的场景，而数据驱动的MOT则在拥有充足带标签训练数据的场景中能实现更优的性能。一个自然的想法是，是否存在一个通用框架能够整合这两种方法。本文提出了一种混合方法，该方法利用神经网络来增强贝叶斯MOT统计模型中已被识别为过于简化的特定方面。通过这种方式，贝叶斯MOT的预测和更新步骤的性能得到了提升。为确保计算的可处理性，我们的框架采用置信传播来避免高维运算，并结合序贯蒙特卡洛方法来高效执行低维运算。最终的方法结合了基于模型方法的灵活性与鲁棒性，以及神经网络从数据中学习复杂信息的能力。我们基于nuScenes自动驾驶数据集对所提方法的性能进行了评估，并证明其具有最先进的性能。