2020/08/17 by Hooman Hedayati, Annika Muehlbradt, Hedayati, Hooman +5 · 1 citation
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Robotics (cs.RO) #cs.CV #cs.HC #cs.RO
paper · pdf · doi:10.48550/arxiv.2008.07668
IROS 2020
arxiv created 2020/08/17 · arxiv updated 2020/08/19
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