A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications
Abstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. Especially for human action recognition based on RGB-D data, a promising research direction, there has been a number of...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-08-01
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| Series: | EURASIP Journal on Image and Video Processing |
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| Online Access: | https://doi.org/10.1186/s13640-025-00677-0 |
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| author | Yumin Zhang Yanyong Wang |
| author_facet | Yumin Zhang Yanyong Wang |
| author_sort | Yumin Zhang |
| collection | DOAJ |
| description | Abstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. Especially for human action recognition based on RGB-D data, a promising research direction, there has been a number of researchers to work on. In particular, convolutional neural networks (CNNs) are capable of image classification tasks, recurrent neural networks (RNNs) are skilled in sequence-based problems, and Transformer is good at global modeling. In this survey, we introduce a number of algorithms based on CNNs, RNNs and Transformer for RGB-D based human action recognition, which could be categorized into four parts: RGB-based, depth-based, skeleton-based and RGB-D based. As a survey focusing on the RGB-D based human action recognition, we thoroughly represent the algorithms, datasets and popular applications for it. What’s more, we give some possible future research directions for this field in the last part. |
| format | Article |
| id | doaj-art-030aed2123e24a8b888ea486dc19d7c2 |
| institution | Kabale University |
| issn | 1687-5281 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj-art-030aed2123e24a8b888ea486dc19d7c22025-08-20T03:43:15ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812025-08-012025116510.1186/s13640-025-00677-0A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applicationsYumin Zhang0Yanyong Wang1Department of Weapon Control System, China North Vehicle Research InstituteDepartment of Weapon Control System, China North Vehicle Research InstituteAbstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. Especially for human action recognition based on RGB-D data, a promising research direction, there has been a number of researchers to work on. In particular, convolutional neural networks (CNNs) are capable of image classification tasks, recurrent neural networks (RNNs) are skilled in sequence-based problems, and Transformer is good at global modeling. In this survey, we introduce a number of algorithms based on CNNs, RNNs and Transformer for RGB-D based human action recognition, which could be categorized into four parts: RGB-based, depth-based, skeleton-based and RGB-D based. As a survey focusing on the RGB-D based human action recognition, we thoroughly represent the algorithms, datasets and popular applications for it. What’s more, we give some possible future research directions for this field in the last part.https://doi.org/10.1186/s13640-025-00677-0Human action recognitionConvolutional neural networksRGB-D dataTransformerRecurrent neural networks |
| spellingShingle | Yumin Zhang Yanyong Wang A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications EURASIP Journal on Image and Video Processing Human action recognition Convolutional neural networks RGB-D data Transformer Recurrent neural networks |
| title | A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications |
| title_full | A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications |
| title_fullStr | A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications |
| title_full_unstemmed | A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications |
| title_short | A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications |
| title_sort | comprehensive survey on rgb d based human action recognition algorithms datasets and popular applications |
| topic | Human action recognition Convolutional neural networks RGB-D data Transformer Recurrent neural networks |
| url | https://doi.org/10.1186/s13640-025-00677-0 |
| work_keys_str_mv | AT yuminzhang acomprehensivesurveyonrgbdbasedhumanactionrecognitionalgorithmsdatasetsandpopularapplications AT yanyongwang acomprehensivesurveyonrgbdbasedhumanactionrecognitionalgorithmsdatasetsandpopularapplications AT yuminzhang comprehensivesurveyonrgbdbasedhumanactionrecognitionalgorithmsdatasetsandpopularapplications AT yanyongwang comprehensivesurveyonrgbdbasedhumanactionrecognitionalgorithmsdatasetsandpopularapplications |