Simplified Regression for Human Pose Estimation
碩士 === 國立清華大學 === 資訊工程學系 === 104 === We present a two-stage deep convolutional neural network for human pose estimation. In the first stage, it directly extracts features from the input image and combines all the features to generate a compact yet effective result for predicting the keypoint locatio...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2016
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| Online Access: | http://ndltd.ncl.edu.tw/handle/76196911503341670670 |
| Summary: | 碩士 === 國立清華大學 === 資訊工程學系 === 104 === We present a two-stage deep convolutional neural network for human pose estimation. In the first stage, it directly extracts features from the input image and combines all the features to generate a compact yet effective result for predicting the keypoint locations instead of producing one heatmap for each keypoint. Then, we use the input image and the synthetic heatmaps derived from the previous stage as the input of the second stage to get a refined result of pose estimation. We evaluate our method on two datasets: FLIC and LSP. Our method achieves the state-of-the-art performance on FLIC dataset.
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