Action recognition method based on fusion of skeleton and apparent features
Focusing on the issue that traditional skeletal feature-based action recognition algorithms were not easy to distinguish similar actions, an action recognition method based on the fusion of deep joints and manual apparent features was considered.The joint spatial position and constraints was firstly...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2022-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022020/ |
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| Summary: | Focusing on the issue that traditional skeletal feature-based action recognition algorithms were not easy to distinguish similar actions, an action recognition method based on the fusion of deep joints and manual apparent features was considered.The joint spatial position and constraints was firstly input into the long short-term memory (LSTM) model equipped with spatio-temporal attention mechanism to acquire spatio-temporal weighted and highly separable deep joint features.After that, heat maps were introduced to locate the key frames and joints, and manually extract the apparent features around the key joints that could be considered as an effective complement to the deep joint features.Finally, the apparent features and the deep skeleton features could be fused frame by frame to achieve effectively discriminating similar actions.Simulation results show that, compared with the state-of-the-art action recognition methods, the proposed method can distinguish similar actions effectively and then the accuracy of action recognition is promoted rather obviously. |
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| ISSN: | 1000-436X |