论文标题
通过多视姿势估计，基于视觉的外围防御
Vision-based Perimeter Defense via Multiview Pose Estimation
论文作者
论文摘要
以前在外围防御游戏中的研究主要集中在完全可观察到的环境上，在该环境中，所有玩家都知道真正的玩家国家。但是，这对于实际实施而言是不现实的，因为捍卫者可能必须感知入侵者并估计其国家。在这项工作中，我们在照片真实的模拟器和现实世界中研究外围防御游戏，要求防御者从视力中估算入侵者国家。我们通过域随机化训练一个基于机器学习的系统，用于入侵者姿势检测，该系统汇总了多个视图以减少状态估计错误并适应防御策略来解决此问题。我们新介绍性能指标来评估基于视觉的外围防御。通过广泛的实验，我们表明我们的方法改善了国家的估计，最终在两场比赛中的VS-1-Intruder游戏和2-Fefenders-VS-1-Intruder游戏中最终进行了外围防御性能。
Previous studies in the perimeter defense game have largely focused on the fully observable setting where the true player states are known to all players. However, this is unrealistic for practical implementation since defenders may have to perceive the intruders and estimate their states. In this work, we study the perimeter defense game in a photo-realistic simulator and the real world, requiring defenders to estimate intruder states from vision. We train a deep machine learning-based system for intruder pose detection with domain randomization that aggregates multiple views to reduce state estimation errors and adapt the defensive strategy to account for this. We newly introduce performance metrics to evaluate the vision-based perimeter defense. Through extensive experiments, we show that our approach improves state estimation, and eventually, perimeter defense performance in both 1-defender-vs-1-intruder games, and 2-defenders-vs-1-intruder games.
