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A Deep Attention Model for Action Recognition from Skeleton Data
| Content Provider | MDPI |
|---|---|
| Author | Gao, Yanbo Li, Chuankun Li, Shuai Cai, Xun Ye, Mao Yuan, Hui |
| Copyright Year | 2022 |
| Description | This paper presents a new IndRNN-based deep attention model, termed DA-IndRNN, for skeleton-based action recognition to effectively model the fact that different joints are usually of different degrees of importance to different action categories. The model consists of (a) a deep IndRNN as the main classification network to overcome the limitation of a shallow RNN network in order to obtain deeper and longer features, and (b) a deep attention network with multiple fully connected layers to estimate reliable attention weights. To train the DA-IndRNN, a new triplet loss function is proposed to guide the learning of the attention among different action categories. Specifically, this triplet loss enforces intra-class attention distances to be smaller than inter-class attention distances and at the same time to allow multiple attention weight patterns to exist for the same class. The proposed DA-IndRNN can be trained end-to-end. Experiments on the widely used datasets, including the NTU RGB + D dataset and UOW Large-Scale Combined (LSC) Dataset, have demonstrated that the proposed method can achieve better and stable performance than the state-of-the-art attention models. |
| Starting Page | 2006 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12042006 |
| Journal | Applied Sciences |
| Issue Number | 4 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-15 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Artificial Intelligence Skeleton-based Action Recognition Indrnn Rnn Attention Model |
| Content Type | Text |
| Resource Type | Article |
