Multiple object tracking is a critical task in autonomous driving. Existing works primarily focus on the heuristic design of neural networks to obtain high accuracy. As tracking accuracy improves, however, neural networks become increasingly complex, posing challenges for their practical application in real driving scenarios due to the high level of latency. In this letter, we explore the use of the neural architecture search (NAS) methods to search for efficient architectures for tracking, aiming for low real-time latency while maintaining relatively high accuracy. Another challenge for object tracking is the unreliability of a single sensor, therefore, we propose a multi-modal framework to improve the robustness. Experiments demonstrate that our algorithm can run on edge devices within lower latency constraints, thus greatly reducing the computational requirements for multi-modal object tracking while keeping lower latency.

中文翻译：

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PNAS-MOT：使用 Pareto 神经架构搜索的多模态对象跟踪

多目标跟踪是自动驾驶中的一项关键任务。现有的工作主要集中在神经网络的启发式设计上以获得高精度。然而，随着跟踪精度的提高，神经网络变得越来越复杂，由于高延迟，给它们在实际驾驶场景中的实际应用带来了挑战。在这封信中，我们探索使用神经架构搜索（NAS）方法来搜索有效的跟踪架构，旨在实现低实时延迟，同时保持相对较高的准确性。目标跟踪的另一个挑战是单个传感器的不可靠性，因此，我们提出了一种多模态框架来提高鲁棒性。实验表明，我们的算法可以在较低延迟限制下在边缘设备上运行，从而大大降低多模式对象跟踪的计算要求，同时保持较低的延迟。