REFORM: 社会的ロボットのためのFフォーメーション認識
REFORM: Recognizing F-formations for Social Robots
人間とエージェントの位置・向きから会話グループ（Fフォーメーション）を機械学習で検出する手法REFORMを提案し、既存手法より高精度であることを示した。
著者: Hooman Hedayati, Annika Muehlbradt, Daniel J. Szafir, Sean Andrist
分類: cs.RO, cs.CV, cs.HC
原文アブストラクト
Recognizing and understanding conversational groups, or F-formations, is a critical task for situated agents designed to interact with humans. F-formations contain complex structures and dynamics, yet are used intuitively by people in everyday face-to-face conversations. Prior research exploring ways of identifying F-formations has largely relied on heuristic algorithms that may not capture the rich dynamic behaviors employed by humans. We introduce REFORM (REcognize F-FORmations with Machine learning), a data-driven approach for detecting F-formations given human and agent positions and orientations. REFORM decomposes the scene into all possible pairs and then reconstructs F-formations with a voting-based scheme. We evaluated our approach across three datasets: the SALSA dataset, a newly collected human-only dataset, and a new set of acted human-robot scenarios, and found that REFORM yielded improved accuracy over a state-of-the-art F-formation detection algorithm. We also introduce symmetry and tightness as quantitative measures to characterize F-formations. Supplementary video: https://youtu.be/Fp7ETdkKvdA , Dataset available at: github.com/cu-ironlab/Babble
