SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos

Abstract

Video-based visual relation detection tasks such as video scene graph generation play important roles in fine-grained video understanding. However current video visual relation detection datasets have two main limitations that hinder the progress of research in this area. First they do not explore complex human-human interactions in multi-person scenarios. Second the relation types of existing datasets have relatively low-level semantics and can be often recognized by appearance or simple prior information without the need for detailed spatio-temporal context reasoning. Nevertheless comprehending high-level interactions between humans is crucial for understanding complex multi-person videos such as sports and surveillance videos. To address this issue we propose a new video visual relation detection task: video human-human interaction detection and build a dataset named SportsHHI for it. SportsHHI contains 34 high-level interaction classes from basketball and volleyball sports. 118075 human bounding boxes and 50649 interaction instances are annotated on 11398 keyframes. To benchmark this we propose a two-stage baseline method and conduct extensive experiments to reveal the key factors for a successful human-human interaction detector. We hope that SportsHHI can stimulate research on human interaction understanding in videos and promote the development of spatio-temporal context modeling techniques in video visual relation detection.