A Deep Learning Based Method For 3D Human Pose Estimation From 2D Fisheye Images
碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 106 === In this study, we propose a deep learning based method to directly estimate the human joint positions in 3D space from 2D fisheye images captured in an egocentric manner. The core of our method is a new design based on Inception-v3 convolutional neural netwo...
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| Format: | Others |
| Language: | en_US |
| Published: |
2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/z9pg52 |
| Summary: | 碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 106 === In this study, we propose a deep learning based method to directly estimate the human joint positions in 3D space from 2D fisheye images captured in an egocentric manner. The core of our method is a new design based on Inception-v3 convolutional neural network featuring the larger convolutional filter size, the reduction of parameters, the long short-term memory module, and the anthropomorphic weights on the training loss. We also conduct four groups of experiments to study the different effects upon the testing results when using different training settings of our work. The experience of our study can be helpful to develop more complicated deep learning network in a reasonable resource requirement to deal with the computer vision problems.
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