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研究生:歐瑞賢
研究生(外文):Ou, Rui- Xian
論文名稱:全天候之人臉與動作辨識及其於睡著與清醒偵測
論文名稱(外文):Day and Night Face and Action Recognition and Its Application to Sleep/Awake Detection
指導教授:張志永
指導教授(外文):Chang, Jyh-Yeong
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電控工程研究所
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:2012
畢業學年度:100
語文別:英文
論文頁數:59
中文關鍵詞:動作辨識人臉辨識睡眠品質偵測模糊推論移動估測近紅外線攝影機
外文關鍵詞:action recognitionface recognitionsleep quality monitoringfuzzy rules inferencemotion estimationNIR cameras
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本論文實現了一套結合人臉辨識、動作辨識與清醒或睡著判別的自動化居家看護系統。首先的人臉與動作辨識工作,待測影像是分別藉由背景相剪法與Haar 疊層分類器產生。為了能抽取出更完整的前景影像,我們分別在灰階與HSV空間建立背景模型。Haar 疊層分類器是一種基於特徵運算的演算法,這種演算法比基於逐點運算的更快速。接著影像將藉由特徵空間與標準空間轉換被投影到一個讓不同類別影像的區別性更大且維度較小的空間。

動作與人臉辨式分別利用模糊法則推論與FisherFace方法來實現。為了將時間軸上的資訊包含進來,我們結合從動作視訊5:1減低抽樣連續三張影像來訓練建立動作辨識模糊法則,並用之推論動作辨識工作。在清醒判別系統中,影像首先會藉由照度隨中心遞減公式來校正。接著利用移動估測方法來量化測試者在睡眠中的活動程度並進一步判定他的清醒/睡著狀態。

In this thesis, we implement an automatic home health care system that combines the face, action and sleep/awake recognition of a person in day and night. The test images are extracted by background subtraction embedded in an action recognition system and then by Haar cascade classifier for face recognition. We build two background models in grayscale and HSV color space to extract the foreground images correctly. Haar cascade classifier for face is a feature-based algorithm that works much faster than the pixel-based algorithm. Then, the test images are transformed to a new space by eigenspace and canonical space projection for better efficiency and separability.

Face and action and recognition is implemented by using FisherFace method and fuzzy rule inference, respectively. We gather three consecutive images 5:1 down-sampled from activity video to construct fuzzy rules inference for containing temporal information to recognize the action. In sleep/awake detection, the LED NIR images will be rectified by using the function of illumination variation firstly. Then, the motion estimation is utilized to quantify the activity degree of a sleeper to determine one’s sleep/awake state.
Contents

摘要……………………………………………………………………………………i
ABSTRACT ……………………………………………………………………………ii
ACKNOWLEDGEMENTS ………………………………………………………………iii
Contents ……………………………………………………………………………iv
List of Figures …………………………………………………………………vii
List of Tables ……………………………………………………………………ix
Chapter 1 Introduction …………………………………………………………1
1.1 Motivation ……………………………………………………………………1
1.2 Face and Activity Recognition System …………………………………2
1.3 Video-Based Sleep/Awake Detection System ……………………………6
1.4 Thesis Outline ………………………………………………………………7

Chapter 2 Face and Action Recognition System ……………………………9
2.1 Foreground Extraction ………………………………………………………9
2.1.1 Background Model …………………………………………………………9
2.1.2 Extraction of Foreground Object ……………………………………11
2.1.3 Shadow suppression ………………………………………………………12
2.1.4 Object Segmentation ……………………………………………………14
2.2 Face Extraction ……………………………………………………………16
2.2.1 Haar Cascade Classifier…………………………………………………16
2.2.2 Skin Detection ……………………………………………………………18
2.3 Fundamentals of Eigenspace and Canonical Space Transform ………20
2.3.1 Eigenspace Transformation (EST) ……………………………………22
2.3.2 Canonical Space Transformation (CST) ………………………………23
2.4 Activity Template Selection ……………………………………………25
2.5 Construction of Fuzzy Rules from Video Stream ……………………27
2.6 Classification algorithm …………………………………………………32

Chapter 3 Video-Based Sleep/Awake Detection System ……………………33
3.1 Image Rectification for Non-uniform Illumination …………………33
3.2 Sleep/Awake Status Detection ……………………………………………36
3.3 Noise Removal ………………………………………………………………38
3.4 Sleeping Posture Recognition ……………………………………………39

Chapter 4 Experimental Results ………………………………………………41
4.1 Image Rectification Result ………………………………………………43
4.2 Foreground Object Extraction ……………………………………………44
4.3 The Day and Night Activity Recognition ………………………………45
4.3.1 Fuzzy Rule Construction ………………………………………………45
4.3.2 The Recognition Rate of Actions ……………………………………47
4.3.3 The Recognition Rate of Faces ………………………………………49
4.4 Sleep/Awake Detection ……………………………………………………53
4.5 Sleeping Posture Recognition ……………………………………………54

Chapter 5 Conclusion ……………………………………………………………57

References …………………………………………………………………………58

References

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[18] Y. C. Luo, “Extracting the Foreground Subject in the HSV Color
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[19] R. Gonzales and R. Woods, Digital Image Processing, 3rd ed.
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