Application of Computer Vision and Vector Space Model for Tactical Movement Classification in Badminton

Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techn...

Full description

Saved in:
Bibliographic Details
Main Authors: Weeratunga, Kokum, Dharmaratne, Anuja, Khoo Boon How
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Data mining
Extraterrestrial measurements
Games
Image segmentation
Trajectory
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techniques to automate data gathering from video footage. The image processing algorithm is validated using video footage of the highest level tournaments, including the Olympic Games. The average accuracy of player position detection is 96.03% and 97.09% on the two halves of a badminton court. Next, frequent trajectories of badminton players are extracted and classified according to their tactical relevance. The classification performs at 97.79% accuracy, 97.81% precision, 97.44% recall, and 97.62% F-score. The combination of automated player position detection, frequent trajectory extraction, and the subsequent classification can be used to automatically generate player tactical profiles.
ISSN:2160-7516
DOI:10.1109/CVPRW.2017.22