Towards Structured Analysis of Broadcast Badminton Videos
Anurag Ghosh, Suriya Singh, C.V. Jawahar

TL;DR
This paper presents an end-to-end framework for automatic analysis and attribute tagging of broadcast badminton videos, enabling large-scale data mining without special equipment.
Contribution
It introduces a novel method for segmenting points, tracking players, and recognizing strokes in badminton videos using standard broadcast footage, without additional sensors.
Findings
Achieved 95.44% point segmentation accuracy
Attained 97.38% player detection score ([email protected])
Stroke segmentation edit score of 80.48%
Abstract
Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation…
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Taxonomy
TopicsVideo Analysis and Summarization · Human Pose and Action Recognition · Sports Analytics and Performance
