AerialMind：迈向无人机场景中的指称多目标跟踪
AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios
AI总结：
AerialMind是首个针对无人机场景的RMOT基准，通过COALA框架和HETrack方法提升无人机的自然语言交互能力与多目标跟踪性能。
摘要：
指称多目标跟踪（RMOT）旨在通过自然语言指令实现精确的目标检测和跟踪，代表了智能机器人系统的基本能力。然而，当前RMOT研究大多局限于地面场景，限制了其捕捉广阔场景上下文和进行全面跟踪和路径规划的能力。相比之下，无人机（UAVs）利用其广阔的空中视角和优越的机动性，能够实现大范围监控。此外，无人机已成为具身智能的关键平台，催生了对具备自然语言交互能力的智能空中系统前所未有的需求。为此，我们引入AerialMind，这是首个针对无人机场景的大型RMOT基准，旨在填补这一研究空白。为了促进其构建，我们开发了创新的半自动化协作代理基于标注助手（COALA）框架，显著降低了劳动成本，同时保持标注质量。此外，我们提出HawkEyeTrack（HETrack），一种新的方法，通过协作增强视觉-语言表示学习并改进无人机场景的感知。全面的实验验证了我们数据集的挑战性和我们方法的有效性。
英文摘要：
Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current RMOT research remains mostly confined to ground-level scenarios, which constrains their ability to capture broad-scale scene contexts and perform comprehensive tracking and path planning. In contrast, Unmanned Aerial Vehicles (UAVs) leverage their expansive aerial perspectives and superior maneuverability to enable wide-area surveillance. Moreover, UAVs have emerged as critical platforms for Embodied Intelligence, which has given rise to an unprecedented demand for intelligent aerial systems capable of natural language interaction. To this end, we introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios, which aims to bridge this research gap. To facilitate its construction, we develop an innovative semi-automated collaborative agent-based labeling assistant (COALA) framework that significantly reduces labor costs while maintaining annotation quality. Furthermore, we propose HawkEyeTrack (HETrack), a novel method that collaboratively enhances vision-language representation learning and improves the perception of UAV scenarios. Comprehensive experiments validated the challenging nature of our dataset and the effectiveness of our method.
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。
