用云语义实现无人机跟踪，支持口头描述、无需训练、高效追踪
CloudTrack: Scalable UAV Tracking with Cloud Semantics
- 基于语义条件的开放词汇追踪，支持颜色等口头描述
- 无需专用训练即可执行任务，适配无人机硬件限制
- 适用于搜救场景，提升追踪效率与生存几率
如今，无人机常用于搜救场景以获取搜索区域信息。自动识别空中影像中被搜寻人员可提高系统自主性、缩短搜索时间，从而增加幸存者获救机会。本文提出一种新型方法，实现语义条件下的开放词汇目标追踪，专为应对无人机硬件限制而设计。该方法支持以衣物颜色等口头描述作为输入，无需专门训练即可执行任务，能有效追踪可能移动的目标。实验结果证明了该方法的通用性与有效性。
原文摘要 · Abstract (English)
Nowadays, unmanned aerial vehicles (UAVs) are commonly used in search and rescue scenarios to gather information in the search area. The automatic identification of the person searched for in aerial footage could increase the autonomy of such systems, reduce the search time, and thus increase the missed person's chances of survival. In this paper, we present a novel approach to perform semantically conditioned open vocabulary object tracking that is specifically designed to cope with the limitations of UAV hardware. Our approach has several advantages. It can run with verbal descriptions of the missing person, e.g., the color of the shirt, it does not require dedicated training to execute the mission and can efficiently track a potentially moving person. Our experimental results demonstrate the versatility and efficacy of our approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。