EDBT 2026 Demo / reviewers in the wild / expert
Xiang Suo
dblp:377/5810
· DBLP profile ↗
8ranked-venue papers
2as first author
8since 2021 · last 2025
0009-0000-5899-0703ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 8 · 2 first-author · 8 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Correction: Digital human and embodied intelligence for sports science: advancements, opportunities and prospects
Xiang Suo, Weidi Tang, Lijuan Mao |
Vis. Comput. | 1 |
| 2025 | Digital human and embodied intelligence for sports science: advancements, opportunities and prospects
Xiang Suo, Weidi Tang, Lijuan Mao |
Vis. Comput. | 1 |
| 2025 | Integrated visual analysis of multi-source data for comprehensive assessment of adolescent physical and mental health
Xunan Tan, Xiang Suo, Fangshu Yao |
Vis. Comput. | 3 |
| 2025 | Data visualization in healthcare and medicine: a survey
Xunan Tan, Xiang Suo, Fangshu Yao |
Vis. Comput. | 2 |
| 2025 | Enhanced dual-model framework for precision player tracking and ball detection in soccer videos
Meng Yang 0011, Jianglang Kang, Xiang Suo, Weiliang Meng, Lijuan Mao, Jun Qi 0001 |
Vis. Comput. | 5 |
| 2025 | A survey on soccer player detection and tracking with videos
Meng Yang 0011, Linlu Jiang, Xiang Suo, Lijuan Mao, Weiliang Meng |
Vis. Comput. | 5 |
| 2024 | Soccer match broadcast video analysis method based on detection and trackingabstractAbstract We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two‐dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high‐speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non‐trivial. To mitigate this, we curate a large‐scale, high‐precision soccer ball detection dataset and devise a robust detection model, which achieved the of 80.9%. Additionally, we develop a high‐speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real‐time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. Meng Yang 0011, Jianglang Kang, Xiang Suo, Weiliang Meng, Lijuan Mao, Bin Sheng 0001, Jun Qi 0001 |
Comput. Animat. Virtual Worlds | 5 |
| 2024 | Soccer player tracking and data correction based on attention with full-field videos
Meng Yang 0011, Linlu Jiang, Xiang Suo, Weiliang Meng, Lijuan Mao |
Vis. Comput. | 5 |