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Ting-Yang Kao

dblp:331/1472 · DBLP profile ↗
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1ranked-venue papers
1as 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 first-author · 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

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking › video analytics
sports video analysis
0.612022
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
0.612022
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
YearPublicationVenuePosition
2022 ScoreActuary: Hoop-Centric Trajectory-Aware Network for Fine-Grained Basketball Shot Analysis
abstract
We 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 Multimedia1