The booming popularity and business value of soccer, worldwide, is driving deeper analysis to obtain precise and elaborate statistics of every soccer player and match. Investigating dribbling skills is beneficial to both train players and to improve defending skills. Dribbling styles can be broadly classified as Stepover, Elastico and Chop. For analysis, in addition to the lack of labeled data in soccer videos there is a lack of fine-grained dribbling styles classification. Additional challenges include the camera calibration, camera motion as a result of player speeds and image registration.
Researchers led by Prof. Bir Bhanu at UCR have designed a patent pending system to automate the classification and analysis of player dribbling styles using an assembled dataset of soccer videos from various sources.
Architecture for the classification of soccer dribbling styles.
The inventors have:
The team have built and tested the prototype on datasets from the top 5 European leagues as well as on synthetic games such as FIFA on Xbox and Playstation. The achieved accuracy of dribbling style classification is 89.83%.
Country | Type | Number | Dated | Case |
United States Of America | Issued Patent | 11,544,928 | 01/03/2023 | 2019-762 |
computer automated analytics, computer vision, convolutional neural networks, soccer