Date of Award
11-18-2021
Document Type
Thesis
Degree Name
Computer Science, MS
First Advisor
Jason Causey
Committee Members
Emily Bellis; Jake Qualls
Call Number
LD 251 .A566t 2021 S56
Abstract
With this thesis we intend to predict the movement of the shuttlecock given ten frames of a video for the each of the next ten frames. We also present a new regression model for the prediction of the frames called PIFR [Present Imputation and Future Regression] which consists of two models prepared for the same. Both the models try to predict the “future” position of the shuttlecock, the players and their rackets. There are two parts of the prediction network: An object detection stage based YOLO and the regression model. [40]
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Singh, Sachleen, "Using Deep Learning to Predict the Path of a Shuttlecock in Badminton" (2021). Student Theses and Dissertations. 295.
https://arch.astate.edu/all-etd/295
