- Title
- Golf Swing Sequencing Using Computer Vision
- Creator
- Marais, Marc, Bradshaw, Karen
- Subject
- To be catalogued
- Date
- 2022
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/464129
- Identifier
- vital:76479
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-04881-4_28"
- Description
- Analysis of golf swing events is a valuable tool to aid all golfers in im-proving their swing. Image processing and machine learning enable an automated system to perform golf swing sequencing using images. The majority of swing sequencing systems implemented involve using ex-pensive camera equipment or a motion capture suit. An image-based swing classification system is proposed and evaluated on the GolfDB dataset. The system implements an automated golfer detector com-bined with traditional machine learning algorithms and a CNN to classify swing events. The best performing classifier, the LinearSVM, achieved a recall score of 88.3% on the entire GolfDB dataset when combined with the golfer detector. However, without golfer detection, the pruned VGGNet achieved a recall score of 87.9%, significantly better (>10.7%) than the traditional machine learning models. The results are promising as the proposed system outperformed a Bi-LSTM deep learning ap-proach to achieve swing sequencing, which achieved a recall score of 76.1% on the same GolfDB dataset. Overall, the results were promising and worked towards a system that can assist all golfers in swing se-quencing without expensive equipment.
- Format
- computer, online resource, application/pdf, 1 online resource (14 pages), pdf
- Publisher
- SpringerLink
- Language
- English
- Relation
- Iberian Conference on Pattern Recognition and Image Analysis, Marais, M. and Brown, D., 2022, April. Golf Swing Sequencing Using Computer Vision. In Iberian Conference on Pattern Recognition and Image Analysis (pp. 351-365). Cham: Springer International Publishing, Iberian Conference on Pattern Recognition and Image Analysis p. 351 2022 1611-3349
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Closed Access
- Hits: 50
- Visitors: 49
- Downloads: 2
Collections
Brown, Dane A (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Golf Swing Sequencing Using Computer Vision.pdf | 2 MB | Adobe Acrobat PDF | View Details |