iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations

Abstract

We present iBall, a basketball video-watching system that leverages gaze-moderated embedded visualizations to facilitate game understanding and engagement of casual fans. Video broadcasting and online video platforms make watching basketball games increasingly accessible. Yet, for new or casual fans, watching basketball videos is often confusing due to their limited basketball knowledge and the lack of accessible, on-demand information to resolve their confusion. To assist casual fans in watching basketball videos, we compared the game-watching behaviors of casual and die-hard fans in a formative study and developed iBall based on the fndings. iBall embeds visualizations into basketball videos using a computer vision pipeline, and automatically adapts the visualizations based on the game context and users' gaze, helping casual fans appreciate basketball games without being overwhelmed. We confrmed the usefulness, usability, and engagement of iBall in a study with 16 casual fans, and further collected feedback from 8 die-hard fans.

ACM CHI23

References in corpus (5)

• YOLOX: Exceeding YOLO Series in 2021
• A Survey on Content-Aware Video Analysis for Sports
• Learning Visual Importance for Graphic Designs and Data Visualizations
• Augmenting Sports Videos with VisCommentator
• GAZED- Gaze-guided Cinematic Editing of Wide-Angle Monocular Video Recordings

Cited by in corpus (4)

• Make Interaction Situated: Designing User Acceptable Interaction for Situated Visualization in Public Environments
• RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios
• User Experience of Visualizations in Motion: A Case Study and Design Considerations
• Reality Proxy: Fluid Interactions with Real-World Objects in MR via Abstract Representations