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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳永耀(Yung-Yaw Chen) | |
| dc.contributor.author | Hung-En Chen | en |
| dc.contributor.author | 陳宏恩 | zh_TW |
| dc.date.accessioned | 2021-06-15T11:31:05Z | - |
| dc.date.available | 2019-08-26 | |
| dc.date.copyright | 2016-08-26 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-08-16 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49488 | - |
| dc.description.abstract | 近年來，監控式攝影機在全球已經越來越普及，致力於將監控系統自動化以辨識景中人物的研究愈加受重視。然而，一般傳統式生物特徵，利如臉、指紋或是虹膜等，都會需要使用者的配合才能取得足夠的資料進行分析；若要將其應用在監控環境中，考量到人物可能會朝向任何方向，所處的距離範圍也可能差異非常大，取得之資料品質會受到嚴重影響。為了解決這一問題，一種稱為軟式特徵的生物特徵正開始發展，例如髮色、種族或是身高等。相較於傳統生物特徵，軟式特徵較不會受距離遠近影響，而且也無需使用者特別的合作就能取得，是一種特別適合在監控環境中使用的生物特徵。而在眾多軟式特徵中，身高是一個較具分辨力且容易被看見的特徵，所以，本論文提出一套基於Kinect V2所提供之人體骨架計算肩膀高度的方法。在人體骨架資訊輸入後，我們會先過濾掉不正確的骨架，以確保後續計算的準確性；在得到初步估算的肩膀高度後，一個經由統計而得的補償方程式可將估算之肩高修正至我們定義之真實肩膀高度。最後，實驗結果顯示出，靜態下的量測平均誤差是16mm，在動態下的量測平均誤差和標準差分別為16mm和19mm。另外，在多人環境下測試的結果指出，即使是在遮蔽頻繁的狀況下，我們的方法仍舊能保持單人環境時的表現。 | zh_TW |
| dc.description.abstract | Interest in the security of individuals has increased in recent years. This trend led to much wider deployment of surveillance cameras both indoor and outdoor. Consequently, more approaches are focusing on improving the mediocre performance of classical biometrics, such as face or iris, under uncontrolled conditions such as illumination and facing directions. To address the problems, soft biometrics, including hair color, ethnicity or height, were proposed. These type of biometrics can be obtained at a distance without subject cooperation, making them ideal for surveillance applications. Among many soft biometric traits, the height trait is one of the most visible and distinctive traits in unconstrained environment. Therefore, an approach to measure human shoulder height based on Kinect V2 skeleton is proposed to assist in human identification. By analyzing the data provided by Kinect V2 skeleton, the preliminary shoulder height can be estimated. Then, a compensation is applied to make the estimated shoulder height match with human true shoulder height. The results show that the accuracy error in static pose is 16mm and the mean absolute error and fluctuation deviation in dynamic pose are 16mm and 19mm, respectively. In addition, the test results in multi-person scenarios show that the proposed approach is more resistant to occlusions, which indicates more applicable in real scenes. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T11:31:05Z (GMT). No. of bitstreams: 1 ntu-105-R02921063-1.pdf: 5871744 bytes, checksum: f95086f6dda9f6fcb1fa103a5f133510 (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES viii LIST OF TABLES xiv Chapter 1 Introduction 1 1.1 Motivation 2 1.2 Problem Formulation 2 1.3 Soft Biometrics 3 1.4 Proposed Approach 5 1.5 Thesis Overview 6 Chapter 2 Previous Work 8 2.1 Biometrics 9 2.1.1 Biometrics overview 10 2.1.2 Soft Biometrics 17 2.2 Body Height Measurement 23 2.3 Summary of Body Height Measurement Methods 28 2.4 Kinect V2 Depth Sensor and Skeleton Detection 29 Chapter 3 Kinect V2 Calibration and Artifacts 31 3.1 Kinect V2 Setup 31 3.2 Point Height Calibration 32 3.2.1 Point Height Estimation 34 3.2.2 Floor Plane Estimation 34 3.2.3 Height Table 37 3.2.4 Interpolation 41 3.2.5 Test Result of Point Height Calibration 42 3.3 Kinect V2 Artifact 45 Chapter 4 Shoulder Height Measurement 48 4.1 Joints Selection from Kinect V2 Skeleton 49 4.1.1 Joints to be compared 49 4.1.2 Testing Design 50 4.1.3 Comparison. 50 4.2 Reliable Skeleton Filtering 54 4.2.1 Outliers Observation 54 4.2.2 Filtering Rules 61 4.3 Kinect V2 Shoulder Joint Analysis and Estimation 67 4.3.1 Impact Factor Analysis 67 4.3.2 Shoulder Height Estimation 73 4.4 Shoulder Height Compensation 74 4.4.1 True Human Shoulder Height Definition 75 4.4.2 Compensation Formulation 76 Chapter 5 Experiment Results 79 5.1 Testing Criteria 80 5.2 Results of Shoulder Height Measurement 83 5.2.1 Static Pose 83 5.2.2 Dynamic Pose 86 5.3 Tests in Multi-person Scenarios 89 5.4 Comparisons and Discussions 93 Chapter 6 Conclusions and Future Work 95 REFERENCES 96 | |
| dc.language.iso | en | |
| dc.title | 利用Kinect V2在監控環境下量測人體肩膀高度 | zh_TW |
| dc.title | Human Shoulder Height Measurement based on Kinect V2 Skeleton under Surveillance Conditions | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林文澧(Win-Li Lin),顏家鈺(Jia-Yush Yen),何明志(Ming-Chih Ho) | |
| dc.subject.keyword | 軟式生物特徵,身高量測,Kinect V2,Kinect V2之人體骨架偵測, | zh_TW |
| dc.subject.keyword | soft biometrics,height estimation,Kinect V2,Kinect skeleton, | en |
| dc.relation.page | 102 | |
| dc.identifier.doi | 10.6342/NTU201602900 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2016-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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