Generating Deeply-Engineered Technical Features for Basketball Video Understanding
Investigating video-guided basketball movement understanding is essential for enhancing sports coaching. Integrating basketball videos with human-computer interaction (HCI) algorithms significantly improves training efficiency. In this paper, we propose a novel method for basketball player motion re...
Saved in:
| Main Authors: | Shaohua Fang, Guifeng Wang, Yongbin Li, Yue Yu, Jun Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10856153/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management
by: Pardis Sadeghi, et al.
Published: (2025-03-01) -
Technical analysis of basketball players' shooting movements through video images
by: Bin Dai, et al.
Published: (2024-12-01) -
A Mobile Image-Driven PM2.5 Estimation Framework Using Deep Learning Techniques
by: Anupam Kamble, et al.
Published: (2025-01-01) -
Deep learning and satellite remote sensing for biodiversity monitoring and conservation
by: Nathalie Pettorelli, et al.
Published: (2025-04-01) -
Basketball shooting motion analysis according to the BEEF concept
by: Ahmad Panji Ramadhan, et al.
Published: (2022-02-01)