Deep State-Space Model for Noise Tolerant Skeleton-Based Action Recognition
| Autor: | Kazuki Kawamura, Kuniaki Uehara, Takashi Matsubara |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
action recognition
business.industry Computer science Deep learning deep learning Skeleton (category theory) state-space models Noise Space model Artificial Intelligence Hardware and Architecture skeleton Action recognition Deep state Computer vision Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Software |
| Zdroj: | IEICE Transactions on Information and Systems. :1217-1225 |
| ISSN: | 1745-1361 0916-8532 |
| Popis: | Action recognition using skeleton data (3D coordinates of human joints) is an attractive topic due to its robustness to the actor's appearance, camera's viewpoint, illumination, and other environmental conditions. However, skeleton data must be measured by a depth sensor or extracted from video data using an estimation algorithm, and doing so risks extraction errors and noise. In this work, for robust skeleton-based action recognition, we propose a deep state-space model (DSSM). The DSSM is a deep generative model of the underlying dynamics of an observable sequence. We applied the proposed DSSM to skeleton data, and the results demonstrate that it improves the classification performance of a baseline method. Moreover, we confirm that feature extraction with the proposed DSSM renders subsequent classifications robust to noise and missing values. In such experimental settings, the proposed DSSM outperforms a state-of-the-art method. |
| Databáze: | OpenAIRE |
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