Demo Data from VLDB 2026 / reviewers in the wild / expert
Chen-Ni Chen
dblp:291/6301
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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.
| Artificial intelligence
1 paper |
Video understanding and tracking · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Video understanding and tracking › video analytics
sports video analysis |
0.6 | 1 | 2022 | ScoreActuary: Hoop-Centric Trajectory-Aware Network for Fine-Grained Basketball Shot Analysis · ACM Multimedia 2022 |
Computer vision › Video understanding and tracking › motion analysis
trajectory analysis |
0.6 | 1 | 2022 | ScoreActuary: Hoop-Centric Trajectory-Aware Network for Fine-Grained Basketball Shot Analysis · ACM Multimedia 2022 |
Methods — techniques the papers use, named apart from their topics
trajectory-aware network · 0.6object detector · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | ScoreActuary: Hoop-Centric Trajectory-Aware Network for Fine-Grained Basketball Shot AnalysisabstractWe propose a fine-grained basketball shot analysis system called ScoreActuary to analyze the players' shot events, which can be applied to game analysis, player training, and highlight generation. Given a basketball video as input, our system first detects/segments shot candidates and then analyzes "Shot Type", "Shot Result", and "Ball Status" of each shot candidate in real-time. Our approach is composed of a customized object detector and a trajectory-aware network to learn the information of ball trajectory. Compared to the existing methods that analyze basketball shots, our algorithm can better handle videos with arbitrary camera movements while improving the accuracy. To the best of our knowledge, this work is the first system that can analyze fine-grained shot events accurately in real basketball games with arbitrary camera movements. Ting-Yang Kao, Tse-Yu Pan, Chen-Ni Chen, Tsung-Hsun Tsai, Hung-Kuo Chu, Min-Chun Hu 0001 |
ACM Multimedia | 3 |