Human Motion Gesture Recognition Based on Computer Vision
Human motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6679746 |
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| author | Rui Ma Zhendong Zhang Enqing Chen |
| author_facet | Rui Ma Zhendong Zhang Enqing Chen |
| author_sort | Rui Ma |
| collection | DOAJ |
| description | Human motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional generation confrontation network to recognize human motion pose. This method uses a deep convolutional stacked hourglass network to accurately extract the location of key joint points on the image. The generation and identification part of the network is designed to encode the first hierarchy (parent) and the second hierarchy (child) and show the spatial relationship of human body parts. The generator and the discriminator are designed as two parts in the network, and they are connected together in order to encode the possible relationship of appearance and, at the same time, the possibility of the existence of human body parts and the relationship between each part of the body and its parental part coding. In the image, the key nodes of the human body model and the general body posture can be identified more accurately. The method has been tested on different data sets. In most cases, the results obtained by the proposed method are better than those of other comparison methods. |
| format | Article |
| id | doaj-art-5ad7fba04e6542768cb9361a4c6eab58 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5ad7fba04e6542768cb9361a4c6eab582025-02-03T01:03:58ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66797466679746Human Motion Gesture Recognition Based on Computer VisionRui Ma0Zhendong Zhang1Enqing Chen2College of Physical Education (school Headquarters), ZhengZhou University, Zhengzhou 450001, ChinaCollege of Physical Education (school Headquarters), ZhengZhou University, Zhengzhou 450001, ChinaCollege of Information Engineering, ZhengZhou University, Zhengzhou 450001, ChinaHuman motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional generation confrontation network to recognize human motion pose. This method uses a deep convolutional stacked hourglass network to accurately extract the location of key joint points on the image. The generation and identification part of the network is designed to encode the first hierarchy (parent) and the second hierarchy (child) and show the spatial relationship of human body parts. The generator and the discriminator are designed as two parts in the network, and they are connected together in order to encode the possible relationship of appearance and, at the same time, the possibility of the existence of human body parts and the relationship between each part of the body and its parental part coding. In the image, the key nodes of the human body model and the general body posture can be identified more accurately. The method has been tested on different data sets. In most cases, the results obtained by the proposed method are better than those of other comparison methods.http://dx.doi.org/10.1155/2021/6679746 |
| spellingShingle | Rui Ma Zhendong Zhang Enqing Chen Human Motion Gesture Recognition Based on Computer Vision Complexity |
| title | Human Motion Gesture Recognition Based on Computer Vision |
| title_full | Human Motion Gesture Recognition Based on Computer Vision |
| title_fullStr | Human Motion Gesture Recognition Based on Computer Vision |
| title_full_unstemmed | Human Motion Gesture Recognition Based on Computer Vision |
| title_short | Human Motion Gesture Recognition Based on Computer Vision |
| title_sort | human motion gesture recognition based on computer vision |
| url | http://dx.doi.org/10.1155/2021/6679746 |
| work_keys_str_mv | AT ruima humanmotiongesturerecognitionbasedoncomputervision AT zhendongzhang humanmotiongesturerecognitionbasedoncomputervision AT enqingchen humanmotiongesturerecognitionbasedoncomputervision |