为无人机视觉追踪设计轻量高效架构与真实评估协议
Architecture and evaluation protocol for transformer-based visual object tracking in UAV applications
- 采用变压器+卡尔曼滤波的模块化异步架构，融合运动补偿与轨迹建模
- 在多个频率下提升成功率与平均失效时间，新指标NT2F验证持续追踪能力
- 提出适配嵌入式设备的评估标准，实测性能更贴近真实部署场景
无人机视觉目标追踪面临平台动态、相机运动和机载资源有限等挑战。现有追踪器或在复杂场景中鲁棒性不足，或计算开销过大难以实时运行。本文提出一种模块化异步追踪架构（MATA），结合基于变压器的追踪器与扩展卡尔曼滤波器，利用稀疏光流进行自身运动补偿，并引入目标轨迹模型。同时提出一种硬件无关、面向嵌入式系统的评估协议，以及新的量化指标「归一化失效时间」（NT2F），衡量追踪器在无外部干预下维持跟踪序列的时长。在包含合成遮挡的增强版UAV123数据集上的实验表明，该方法在多种处理频率下均显著提升成功率与NT2F指标。基于Nvidia Jetson AGX Orin的ROS 2实现验证了该评估协议更贴近真实嵌入式系统表现。
原文摘要 · Abstract (English)
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.
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