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; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22087-22097

Abstract

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.

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[bibtex]
@InProceedings{Jahangard_2024_CVPR, author = {Jahangard, Simindokht and Cai, Zhixi and Wen, Shiki and Rezatofighi, Hamid}, title = {JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {22087-22097} }