| Author: | He, Jiacheng |
| Title: | Deep learning in basketball action recognition |
| Advisors: | Chi, Zheru (EIE) |
| Degree: | M.Sc. |
| Year: | 2021 |
| Subject: | Human activity recognition Pattern recognition systems Image processing -- Digital techniques Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Electronic and Information Engineering |
| Pages: | iv, 44 pages : color illustrations |
| Language: | English |
| Abstract: | Basketball is a sport that many people love, and I also like it very much. There are many different actions in this sport, such as shooting, dribbling, passing, etc. In daily competitions, these data are calculated manually. If the deep learning model can be applied to basketball action recognition, it can replace some manual data processing and data entries, make it more intelligent, and bring convenience to the audience to watch the game. And the technique can be extended to other sports with a good prospect. 3D CNNs are very powerful at extracting features in videos and applying them. It is suitable for the action recognition of basketball mentioned above. Convolutional neural networks with spatio-temporal 3D kernels (3D CNNs) are used to extract spatio-temporal features in the video to recognize the actions of sport men/women in the video. The model trained by the 3D kernel is prone to overfitting due to its large number of parameters. Therefore, a model combining residual networks (ResNets) with 3D CNNs, which has excellent performance in handling over-fitting problems in 2D convolutional learning, achieve a relatively better performance. This dissertation introduces the use of 3D ResNets to train SpaceJam, which is a dataset for basketball action recognition and show its training and test results. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5659.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.2 MB | Adobe PDF | View/Open |
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