JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups
Simindokht Jahangard, Zhixi Cai, Shiki Wen, Hamid Rezatofighi
Understanding human social behaviour is crucial in computer vision and robotics. Micro-level observations like individual actions fall short necessitating a comprehensive approach that considers individual behaviour intra-group dynamics and social group levels for a thorough understanding. To address dataset limitations this paper introduces JRDB-Social an extension of JRDB. Designed to fill gaps in human understanding across diverse indoor and outdoor social contexts JRDB-Social provides annotations at three levels: individual attributes intra-group interactions and social group context. This dataset aims to enhance our grasp of human social dynamics for robotic applications. Utilizing the recent cutting-edge multi-modal large language models we evaluated our benchmark to explore their capacity to decipher social human behaviour.
