Real-time Human Pose Estimation from Single Depth Images via Low-Cost Platform
| Autor: | Ting-Chun Liu, 劉庭君 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 In recent years, the rapid development of the technology, human-computer interaction devices is becoming a hot issue that discussed and developed widely. In- stead of employing the traditional peripheral products such as buttons and keyboard as input, users can interact and communicate with the machine through the body directly, this not only makes the operation more diverse and various but also makes it much easier for users to get into scenarios. However, it is complicated and dicult to judge the human body through the camera correctly and it is necessary to judge the movement in real-time, on the other hand, it is also important to achieve high identication ability, so it has been the hot issue of discussion and research in the eld of computer vision. In this thesis, we proposed a system of human pose estimation that can be applied to dance machine or the games of Virtual Reality(VR) in low-cost hardware equipment. In our study, we employed depth images as training resource, and we also employed the popular deep learning method: Convolutional Neural Network(CNN) to be our architecture to detect 12 joints of the human body automatically. Since employed depth images, we have explored the performance and characteristics of the images that taken by dierent depth cameras and collected various depth maps as training sets, which make our machine identify positions of joints more accurately. This study was composed of four parts, the rst part is the database of the depth images we employed. The second part is the labeling of the joints and the adjustment method in our research. The third part is doing the discussion of CNN architecture we implement. The last part is to explore the evaluation system at the present time. Including OKS and PDJ evaluation. In the experimental part, we employed the depth images by RealSense camera to be input in our system, doing the experiment in indoor and in video arcade, that showed the positions of joints immediately. Finally, we employed OKS and PDJ to be our validation methods to judge our skeleton evaluation system that can achieve 93.1% and 79.0%, with the impressive result of identication, we implement the model we trained into a real-time detection and results showed that the system we proposed is reliable. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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