Abstract
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 paper, 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.
Abstract (translated)
multiple object tracking是自动驾驶中的一个关键任务。现有的工作主要集中在神经网络的启发式设计以获得高精度的精确度。然而，随着跟踪准确度的提高，神经网络变得越来越复杂，这对其在现实驾驶场景中的实际应用造成了延迟。在本文中，我们探讨了使用神经架构搜索（NAS）方法来寻找跟踪的高效架构，旨在保持较低的实时延迟，同时保持相对较高的精度。另一个挑战是物体跟踪的不确定性，因此我们提出了一个多模态框架来提高其鲁棒性。实验证明，我们的算法可以在较低的延迟约束下运行边缘设备，从而大大减少多模态物体跟踪的计算需求，同时保持较低的延迟。
URL
https://arxiv.org/abs/2403.15712
