Deep dynamic feature alignment-based few-shot learning for figure skating action localization

Mingyang Wang; Yiqun Pang; Qiurui Wang; Dan Chen

doi:10.1117/12.2684901

19 October 2023 Deep dynamic feature alignment-based few-shot learning for figure skating action localization

Mingyang Wang, Yiqun Pang, Qiurui Wang, Dan Chen

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127096C (2023) https://doi.org/10.1117/12.2684901
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Temporal action localization is a challenging and important task in computer vision. Although great progress has been made in recent years in this area with the help of deep learning, few jobs focus on applications on competitive sports, such as figure skating. To better analyze the players’ actions in figure skating, action localization is needed. Hence we collect 82 videos from worldwide figure skating contests, including championship and grand prix, etc. These videos are annotated by experts for action start timestamps, action end timestamps, and action names. We further analyze these videos by our proposed Deep Dynamic Feature Alignment (DDFA) method, which employs deep convolutional features and learns the action pattern between the target action and reference action. Experiments on models’ performances and results based on these datasets are presented, showing the proposed methods can predict proper action candidates for raw figure skating videos.

Citation Download Citation

Mingyang Wang, Yiqun Pang, Qiurui Wang, and Dan Chen "Deep dynamic feature alignment-based few-shot learning for figure skating action localization", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127096C (19 October 2023); https://doi.org/10.1117/12.2684901

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available