| Autor: |
Changwei Ouyang, Yun Yi, Hanli Wang, Jin Zhou, Tao Tian |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of Imaging, Vol 10, Iss 9, p 216 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2313-433X |
| DOI: |
10.3390/jimaging10090216 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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