The Study of Action Recognition in RGB-D Videos
| Autor: | Hsin-Yi Lin, 林欣毅 |
|---|---|
| Rok vydání: | 2016 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 104 In this thesis, we investigate the effects of sparse representation for RGB-D action recognition, especially for human fall detection. The sparse coding has shown impressive results of action recognition in RGB video. However, the performance of the sparse coding on RGB-D action recognition has not been thoroughly investigated. In this research, we propose an approach to intelligently combine both advantages of depth and skeleton information. Also, a two-level feature learning scheme using sparse coding is introduced for skeleton information. First, we extract features, called DMM-HOG and Moving Pose Descriptor, from the depth and skeleton respectively. Next, we use the pyramid temporal pooling to convert the Moving Pose Descriptor into a compact vector. Then we apply the sparse coding to encode both descriptors. Finally, we combine classification results of depth and skeleton by the logistic regression. To evaluate the performance of the proposed approach, we test the proposed approach on two public available fall detection datasets and achieve the highest accuracy in comparison with other methods. Also, we evaluate the proposed approach on a public available RGB-D action recognition dataset, MSR Action3D. The proposed approach achieve a competitive result in comparison with the state-of-the-art approaches. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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