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Jiarui Shan

dblp:342/2760 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-2304-2129ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 77% Multimedia analysis and retrieval · 23%
Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › information visualization
embedded visualization
0.712023
iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations · CHI 2023
Wearable and physiological sensing › eye tracking
gaze-based interaction
0.712023
iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations · CHI 2023
Multimedia analysis and retrieval
sports video analysis
0.212023
iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations · CHI 2023

Methods — techniques the papers use, named apart from their topics

computer vision · 1.3
YearPublicationVenuePosition
2023 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 findings. 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 confirmed the usefulness, usability, and engagement of iBall in a study with 16 casual fans, and further collected feedback from 8 die-hard fans.
Chen Zhu-Tian, Qisen Yang, Jiarui Shan, Tica Lin, Johanna Beyer, Haijun Xia, Hanspeter Pfister
CHI3