Sequence Segmentation Attention Network for Skeleton-Based Action Recognition
With skeleton-based action recognition, it is crucial to recognize the dependencies among joints. However, the current methods are not able to capture the relativity of the various joints among the frames, which is extremely helpful because various parts of the body are moving at the same time. In o...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/7/1549 |
| _version_ | 1797608123995783168 |
|---|---|
| author | Yujie Zhang Haibin Cai |
| author_facet | Yujie Zhang Haibin Cai |
| author_sort | Yujie Zhang |
| collection | DOAJ |
| description | With skeleton-based action recognition, it is crucial to recognize the dependencies among joints. However, the current methods are not able to capture the relativity of the various joints among the frames, which is extremely helpful because various parts of the body are moving at the same time. In order to solve this problem, a new sequence segmentation attention network (SSAN) is presented. The successive frames are encoded in each of the segments that make up the skeleton sequence. Then, we provide a self-attention block that may record the associated information among various joints in successive frames. In order to better recognize comparable behavior, a model of external segment action attention is employed to acquire the deep interrelation information among parts. Compared with the most advanced approaches, we have shown that the proposed method performs better on NTU RGB+D and NTU RGB+D 120. |
| first_indexed | 2024-03-11T05:40:12Z |
| format | Article |
| id | doaj.art-c8de79a04c72492abc7d9b3a856375a2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T05:40:12Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c8de79a04c72492abc7d9b3a856375a22023-11-17T16:32:11ZengMDPI AGElectronics2079-92922023-03-01127154910.3390/electronics12071549Sequence Segmentation Attention Network for Skeleton-Based Action RecognitionYujie Zhang0Haibin Cai1Software Engineering Institute, East China Normal University, Shanghai 200062, ChinaSoftware Engineering Institute, East China Normal University, Shanghai 200062, ChinaWith skeleton-based action recognition, it is crucial to recognize the dependencies among joints. However, the current methods are not able to capture the relativity of the various joints among the frames, which is extremely helpful because various parts of the body are moving at the same time. In order to solve this problem, a new sequence segmentation attention network (SSAN) is presented. The successive frames are encoded in each of the segments that make up the skeleton sequence. Then, we provide a self-attention block that may record the associated information among various joints in successive frames. In order to better recognize comparable behavior, a model of external segment action attention is employed to acquire the deep interrelation information among parts. Compared with the most advanced approaches, we have shown that the proposed method performs better on NTU RGB+D and NTU RGB+D 120.https://www.mdpi.com/2079-9292/12/7/1549human action recognitionskeleton dataself-attentionattention mechanism |
| spellingShingle | Yujie Zhang Haibin Cai Sequence Segmentation Attention Network for Skeleton-Based Action Recognition Electronics human action recognition skeleton data self-attention attention mechanism |
| title | Sequence Segmentation Attention Network for Skeleton-Based Action Recognition |
| title_full | Sequence Segmentation Attention Network for Skeleton-Based Action Recognition |
| title_fullStr | Sequence Segmentation Attention Network for Skeleton-Based Action Recognition |
| title_full_unstemmed | Sequence Segmentation Attention Network for Skeleton-Based Action Recognition |
| title_short | Sequence Segmentation Attention Network for Skeleton-Based Action Recognition |
| title_sort | sequence segmentation attention network for skeleton based action recognition |
| topic | human action recognition skeleton data self-attention attention mechanism |
| url | https://www.mdpi.com/2079-9292/12/7/1549 |
| work_keys_str_mv | AT yujiezhang sequencesegmentationattentionnetworkforskeletonbasedactionrecognition AT haibincai sequencesegmentationattentionnetworkforskeletonbasedactionrecognition |