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研究生:周佳蓉
研究生(外文):Chou, Chia-Jung
論文名稱:用於人體姿勢估測之對抗式訓練方法
論文名稱(外文):Self Adversarial Training for Human Pose Estimation
指導教授:陳煥宗
指導教授(外文):Chen, Hwann-Tzong
口試委員:賴尚宏劉庭祿
口試委員(外文):Lai, Shang-HongLiu, Tyng-Luh
口試日期:2017-06-29
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:36
中文關鍵詞:人體姿勢估測對抗式生成網路全卷積類神經網路
外文關鍵詞:Human Pose EstimationGenerative Adversarial NetworkFully Convolutional Neural Network
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本篇論文提出一個基於深度學習,用於人體姿勢估測的方法。我們採用生成對抗網
路作為我們學習模式,其中我們建立了兩個堆疊式沙漏型網路,一個作為生成網
路,一個作為鑑別網路。訓練完成後,生成網路會直接當作人體姿勢估測使用。鑑
別網路用來區分標準答案的熱圖和生成的熱圖,以此計算出的對抗損失反向回饋至
生成網路。此過程能使生成網路學習人體姿勢合理性,且從實驗結果發現,這樣的
訓練有助於提升預測的準確度。
This thesis presents a deep learning based approach to the problem of human pose
estimation. We employ generative adversarial networks as our learning paradigm in
which we set up two stacked hourglass networks with the same architectures, one as
the generator and the other as the discriminator. The generator is used as a human
pose estimator after the training is done. The discriminator distinguishes groundtruth
heatmaps from generated ones, and back-propagates the adversarial loss to the
generator. This process enables the generator to learn the plausible human body
congurations and is shown to be useful for improving the prediction accuracy.
1 Introduction 8
2 Related Work 10
2.1 Human Pose Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Generative Adversarial Networks . . . . . . . . . . . . . . . . . . . . 11
3 Adversarial Training with the Stacked Hourglass Networks 13
3.1 Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1.1 Network Architecture . . . . . . . . . . . . . . . . . . . . . . . 14
3.1.2 Training the Generator . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Discriminator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2.1 Training the Discriminator . . . . . . . . . . . . . . . . . . . . 17
3.3 Adversarial Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4 Experiments 21
4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.3.1 LSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.3.2 MPII . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.3.3 LIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.4 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5 Conclusion 32
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