一個基於行走姿勢特徵的步態辨識系統

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吳郁樟(Yu-Chang Wu) 查詢紙本館藏

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資訊工程學系在職專班

論文名稱

一個基於行走姿勢特徵的步態辨識系統
(A Gait Recognition System Based on Walking Posture Features)

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摘要(中)

在多種生物辨識技術中，步態特徵能夠在中距離進行辨識，適用於街道監控系統，在常見的步態辨識方法中經常使用背景建模方法，此方法遇到複雜的動態背景，會使得前景提取困難，由於人體姿勢特徵不易受到動態背景的影響，因此本研究基於人體姿勢特徵，設計一個步態辨識系統，其中包含人體姿勢偵測、行人追蹤、步行週期偵測、建立步態特徵和步態辨識。人體姿勢偵測使用OpenPose，行人追蹤使用行人重識別方法，步行週期偵測使用人體腳踝偵測方法，步態辨識本研究結合人體姿勢與光流特徵，並提出人體骨架遮罩方法來去除部分的光流背景雜訊，在遮罩部分保留了骨架輪廓特徵，光流特徵則保留運動方向，最後使用CNN步態辨識方法與CASIA Dataset B步態資料庫進行訓練，步態辨識率為85.5，驗證此方法是有效的。

摘要(英)

Among various types of biometrics, gait analysis is used for middle-distance gait recognition and is suitable for street surveillance system. A common gait recognition method is background subtraction. In this method, foreground extraction is difficult if the background is complex and dynamic. Because the features of human poses are not easily influenced by dynamic backgrounds, this study designed a gait recognition system based on human pose features. The system included functions of human pose detection, pedestrian tracking, and walking cycle detection and enabled the establishment of gait features and gait recognition. Human pose detection was performed using OpenPose; pedestrian tracking was conducted through person re-identification; and walking cycles were detected using the data of human ankles. For gait recognition, this study combined human poses and optical flow features, proposing a mask method for human skeletons to remove a part of the noises in the optical flow background. Features of skeleton outlines were retained from masks, and movement directions were retained using optical flow features. Finally, this study adopted convolutional neural network gait recognition method and the Dataset B of Chinese Academy of Sciences Gait Database for data training. The obtained gait recognition rate was 85.5%, confirming that the proposed method was effective.

關鍵字(中)

★ 步態辨識

關鍵字(英)

★ Gait Recognition

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 論文架構 3
第二章文獻回顧 4
2.1 經典步態特徵回顧 4
2.1.1 基於輪廓 4
2.1.2 基於3D模型 6
2.1.3 基於深度攝影機 7
2.2 經典步態分類方法回顧 8
2.2.1 基於最近鄰居 8
2.2.2 基於支持向量機 9
2.3 基於神經網路步態辨識 10
2.4 神經網路基礎理論 11
2.4.1 激勵函數 13
2.4.2 優化函數 15
2.5 卷積神經網路 16
2.6 神經網路訓練方法 17
2.6.1 開集合的Identification方法 17
2.6.2 開集合的Verification方法 18
2.6.3 閉集合的Identification方法 18
2.6.4 閉集合的Verification方法 19
2.7 人體姿勢演算法回顧 20
2.7.1 AlphaPose 20
2.7.2 DensePose 21
第三章步態辨識系統設計 23
3.1 步態資料庫 23
3.2 步態辨識架構 23
3.3 建立人體姿勢 25
3.3.1 OpenPose 25
3.3.2 行人辨識 27
3.4 行人追蹤 27
3.5 步行週期偵測 30
3.6 建立步態特徵 30
3.6.1 行人圖像校正 30
3.6.2 人體姿勢與熱圖特徵 31
3.6.3 光流特徵 32
3.6.4 光流與人體遮罩特徵 33
3.7 CNN步態辨識 35
第四章實驗結果 37
4.1 實驗方法 37
4.2 開發環境 37
4.3 實驗結果 38
4.3.1 驗證方法 38
4.3.2 步態特徵實驗 39
第五章結論 44
5.1 結論 44
5.2 未來展望 45
參考文獻 46

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指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2019-1-30

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