自主无人机群中涌现的多视角保真度
Emergent Multi-View Fidelity in Autonomous UAV Swarm Sport Injury Detection
AI总结：
本文提出利用无人机群自动实现多视角碰撞检测，以提高运动学提取的准确性和冗余性，无需无人机间通信。
摘要：
准确、实时的碰撞检测对于确保 rugby 等高接触性体育运动中运动员安全和有效裁判至关重要，尤其是在与创伤性脑损伤（TBI）相关的严重风险情况下。传统碰撞监测方法使用固定摄像头或可穿戴传感器在可见性、覆盖范围和响应性方面存在局限。此前，我们提出了一种利用无人驾驶航空器（UAVs）进行监测和实时运动学提取的框架。在本文中，我们表明，旨在确保至少一个 UAV 能够捕捉比赛上每个事件的策略具有涌现属性，能够满足成功运动学提取的关键条件。即，它们确保几乎所有的碰撞都被多个无人机捕捉，建立多视角保真度和冗余性，而无需任何无人机之间的通信。
英文摘要：
Accurate, real-time collision detection is essential for ensuring player safety and effective refereeing in high-contact sports such as rugby, particularly given the severe risks associated with traumatic brain injuries (TBI). Traditional collision-monitoring methods employing fixed cameras or wearable sensors face limitations in visibility, coverage, and responsiveness. Previously, we introduced a framework using unmanned aerial vehicles (UAVs) for monitoring and real time kinematics extraction from videos of collision events. In this paper, we show that the strategies operating on the objective of ensuring at least one UAV captures every incident on the pitch have an emergent property of fulfilling a stronger key condition for successful kinematics extraction. Namely, they ensure that almost all collisions are captured by multiple drones, establishing multi-view fidelity and redundancy, while not requiring any drone-to-drone communication.
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。
