无人机群通过单机覆盖策略，自动实现多视角冗余捕捉碰撞事件。
Emergent Multi-View Fidelity in Autonomous UAV Swarm Sport Injury Detection
- 以确保每起碰撞至少被一架无人机捕获为目标，设计分布式监控策略。
- 实验显示98%以上碰撞事件被多架无人机同时拍摄，实现自然多视角覆盖。
- 无需无人机间通信，适合实时高危体育赛事的伤病检测应用。
在橄榄球等高强度对抗性运动中，准确、实时的碰撞检测对保障运动员安全和公正判罚至关重要，尤其考虑到创伤性脑损伤（TBI）的严重风险。传统的固定摄像头或可穿戴传感器监测方法存在视野受限、覆盖不足和响应延迟等问题。此前，我们提出一种基于无人机（UAV）的框架，用于实时提取碰撞事件的运动学数据。本文表明，仅以确保每起事件至少被一架无人机捕获为优化目标的策略，会涌现出更强的关键特性：几乎所有碰撞事件均被多架无人机同时捕捉，实现了多视角保真度与冗余性，且无需任何无人机间的通信。该机制显著提升了运动学重建的可靠性，同时保持系统低复杂度与高可扩展性。
原文摘要 · Abstract (English)
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.
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