利用单帧标注训练实时追踪低帧率无人机拍摄的军用车辆。
Self-Supervised Real-Time Tracking of Military Vehicles in Low-FPS UAV Footage
- 基于单帧标注学习实例关联，提升低帧率视频追踪能力。
- 全局场景特征使追踪在检测缺失时仍保持高精度。
- 模型压缩后推理速度更快，适合实战部署场景。
多目标追踪（MOT）旨在跨视频帧维持对象的一致身份。在实际作战场景中，由移动无人机拍摄的低帧率视频因物体外观和位置快速变化，且受云视频流传输与压缩算法导致的图像退化，使得追踪尤为困难。本文提出通过单帧标注进行实例关联学习，克服上述挑战。研究发现，场景的全局特征为低帧率实例关联提供了关键上下文信息，使方法对干扰项和检测间断具有鲁棒性。同时，该追踪方法在降低输入图像分辨率和潜在表示尺寸的情况下仍能保持高质量关联。最后，本文构建了一个公开数据源采集的标注军事车辆基准数据集。本论文最初发表于2025年5月13-14日在葡萄牙奥埃拉斯举行的北约科技组织研讨会（ICMCIS），由信息系统技术（IST）科学与技术委员会主办，项目编号IST-209-RSY。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) aims to maintain consistent identities of objects across video frames. Associating objects in low-frame-rate videos captured by moving unmanned aerial vehicles (UAVs) in actual combat scenarios is complex due to rapid changes in object appearance and position within the frame. The task becomes even more challenging due to image degradation caused by cloud video streaming and compression algorithms. We present how instance association learning from single-frame annotations can overcome these challenges. We show that global features of the scene provide crucial context for low-FPS instance association, allowing our solution to be robust to distractors and gaps in detections. We also demonstrate that such a tracking approach maintains high association quality even when reducing the input image resolution and latent representation size for faster inference. Finally, we present a benchmark dataset of annotated military vehicles collected from publicly available data sources. This paper was initially presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST)Scientific and Technical Committee, IST-209-RSY - the ICMCIS, held in Oeiras, Portugal, 13-14 May 2025.
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