Abstract
Object tracking from Unmanned Aerial Vehicles (UAVs) is challenged by platform dynamics, camera motion, and limited onboard resources. Existing visual trackers either lack robustness in complex scenarios or are too computationally demanding for real-time embedded use. We propose an Modular Asynchronous Tracking Architecture (MATA) that combines a transformer-based tracker with an Extended Kalman Filter, integrating ego-motion compensation from sparse optical flow and an object trajectory model. We further introduce a hardware-independent, embedded oriented evaluation protocol and a new metric called Normalized time to Failure (NT2F) to quantify how long a tracker can sustain a tracking sequence without external help. Experiments on UAV benchmarks, including an augmented UAV123 dataset with synthetic occlusions, show consistent improvements in Success and NT2F metrics across multiple tracking processing frequency. A ROS 2 implementation on a Nvidia Jetson AGX Orin confirms that the evaluation protocol more closely matches real-time performance on embedded systems.
Abstract (translated)
来自无人飞行器（UAV）的对象跟踪面临平台动态、相机运动和有限的机载资源等挑战。现有的视觉追踪方法要么在复杂场景中不够鲁棒，要么对实时嵌入式应用来说计算需求过高。我们提出了一种模块化异步跟踪架构（MATA），该架构结合了基于变压器的追踪器和扩展卡尔曼滤波器，并集成了稀疏光流带来的自我运动补偿以及对象轨迹模型。此外，我们还引入了一种硬件无关、面向嵌入式设备的评估协议，以及一种新的度量标准——归一化失效时间（NT2F），用以量化追踪器在没有外部帮助的情况下能够维持跟踪序列的时间长度。实验结果表明，在包括增强版UAV123数据集（该数据集中合成了一些遮挡情况）在内的多个无人机基准测试中，MATA方法在成功率和NT2F度量标准上都有显著的改进，并且这些改进在不同的处理频率下是一致的。基于Nvidia Jetson AGX Orin设备上的ROS 2实现表明，这种评估协议更贴近嵌入式系统中的实时性能。 这段文字描述了一种新的对象跟踪架构以及相关的实验结果和评价方法，适用于无人飞行器（UAV）等场景下的视觉追踪任务。
URL
https://arxiv.org/abs/2603.03904
