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.

通过引入 JRDB-Social 数据集，本研究利用最新的多模态大语言模型来评估其解读社会人类行为能力，以加深我们对于人类社会动态在机器人应用中的理解。