A hybrid neural network-based intelligent body posture estimation system in sports scenes.
Math Biosci Eng
; 21(1): 1017-1037, 2024 Jan.
Article
en En
| MEDLINE
| ID: mdl-38303452
ABSTRACT
Body posture estimation has been a hot branch in the field of computer vision. This work focuses on one of its typical applications recognition of various body postures in sports scenes. Existing technical methods were mostly established on the basis of convolution neural network (CNN) structures, due to their strong visual information sensing ability. However, sports scenes are highly dynamic, and many valuable contextual features can be extracted from multimedia frame sequences. To handle the current challenge, this paper proposes a hybrid neural network-based intelligent body posture estimation system for sports scenes. Specifically, a CNN unit and a long short-term memory (LSTM) unit are employed as the backbone network in order to extract key-point information and temporal information from video frames, respectively. Then, a semi-supervised learning-based computing framework is developed to output estimation results. It can make training procedures using limited labeled samples. Finally, through extensive experiments, it is proved that the proposed body posture estimation method in this paper can achieve proper estimation effect in real-world frame samples of sports scenes.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Postura
/
Redes Neurales de la Computación
Idioma:
En
Revista:
Math Biosci Eng
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Estados Unidos