FineTea: A Novel Fine-Grained Action Recognition Video Dataset for Tea Ceremony Actions.
J Imaging
; 10(9)2024 Aug 31.
Article
em En
| MEDLINE
| ID: mdl-39330436
ABSTRACT
Methods based on deep learning have achieved great success in the field of video action recognition. When these methods are applied to real-world scenarios that require fine-grained analysis of actions, such as being tested on a tea ceremony, limitations may arise. To promote the development of fine-grained action recognition, a fine-grained video action dataset is constructed by collecting videos of tea ceremony actions. This dataset includes 2745 video clips. By using a hierarchical fine-grained action classification approach, these clips are divided into 9 basic action classes and 31 fine-grained action subclasses. To better establish a fine-grained temporal model for tea ceremony actions, a method named TSM-ConvNeXt is proposed that integrates a TSM into the high-performance convolutional neural network ConvNeXt. Compared to a baseline method using ResNet50, the experimental performance of TSM-ConvNeXt is improved by 7.31%. Furthermore, compared with the state-of-the-art methods for action recognition on the FineTea and Diving48 datasets, the proposed approach achieves the best experimental results. The FineTea dataset is publicly available.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
J Imaging
Ano de publicação:
2024
Tipo de documento:
Article