基于变压器的视觉目标跟踪架构及评估协议：无人机应用
Architecture and evaluation protocol for transformer-based visual object tracking in UAV applications
AI总结：
本文提出MATA架构结合变压器跟踪器与扩展卡尔曼滤波器，引入硬件无关的嵌入式评估协议和NT2F指标，提升无人机复杂场景下的跟踪鲁棒性和实时性能。
摘要：
无人机上的目标跟踪面临平台动态、相机运动和有限计算资源的挑战。现有视觉跟踪器在复杂场景中缺乏鲁棒性或计算成本过高。本文提出模块化异步跟踪架构（MATA），结合基于变压器的跟踪器与扩展卡尔曼滤波器，整合稀疏光流的自我运动补偿和目标轨迹模型。进一步引入硬件无关、面向嵌入式的评估协议和新的指标Normalized time to Failure（NT2F），以量化跟踪器在无外部帮助情况下维持跟踪序列的时间长度。在无人机基准测试集上，包括增强版UAV123数据集（含合成遮挡）的实验表明，多跟踪处理频率下成功率和NT2F指标均有所提升。在Nvidia Jetson AGX Orin上的ROS 2实现证实，该评估协议更贴近嵌入式系统的实时性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
