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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
dc.contributor.author | Shu-Chun Lin | en |
dc.contributor.author | 林叔君 | zh_TW |
dc.date.accessioned | 2021-06-15T16:15:12Z | - |
dc.date.available | 2018-08-25 | |
dc.date.copyright | 2015-08-25 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-17 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52452 | - |
dc.description.abstract | In this thesis, two novel features for activity recognition from top-view depth image sequences are firstly proposed. Most of previous works are focusing mainly on dealing with the side-view depth image sequences, which unfortunately may encounter occlusion problems. Therefore, top-view camera setting is adopted in our thesis. Based on the notion of computed tomography, the top-view depth images are segmented to different layer along z-axis. Then, the representative body points which are found on each layered image will be a meaningful feature as the substitute of body parts for the activity postures. Besides, a discriminative shape descriptor is also proposed to describe the human shape for different activity postures. Based on the occupancy value of small region, the cylinders-sector occupancy grid with saturation function is proposed to capture special characteristic of top-view human shape. To make our proposed features invariant to orientation, the human orientation is also calculated by extracting the regions of head and shoulders, and then refines the above two features according to the orientation. Finally, dynamic time warping algorithm is applied to address the problem with different sequence lengths and the SVM classifier is trained to classify our activities. To verify our performance, 2 new top-view datasets are constructed. In our experiments, challenging cross-subject tests are conducted, and the effectiveness of our representative body points and layered sector-based shape descriptor are demonstrated. The result shows that the accuracy can achieve up to 96%, which is quite promising while being compared with those from the state-of-the-art methods in the literature. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T16:15:12Z (GMT). No. of bitstreams: 1 ntu-104-R02921013-1.pdf: 2638961 bytes, checksum: 3b75c0b814cf7a226c521a6729facc57 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 口試委員會審定書 #
中文摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES ix LIST OF TABLES xiii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Problem Formulation 5 1.3 Literature Review 6 1.3.1 Applications based on top-view camera settings 6 1.3.2 Activity recognition using depth image sequences 7 1.4 Contribution 9 1.5 Thesis Organization 10 Chapter 2 Preliminaries and the System Configuration 12 2.1 Dynamic Time Warping (DTW) 13 2.1.1 Principle of DTW 13 2.1.2 Classification by DTW 16 2.1.3 Advantages and Disadvantages of classification by DTW 16 2.2 Support Vector Machine (SVM) 17 2.2.1 Linear SVM 18 2.2.2 General form of linear SVM 20 2.2.3 Soft Margin SVM 22 2.2.4 Nonlinear SVM 23 2.3 System Design and Preprocessing 24 2.3.1 Camera Environment Setting 25 2.3.2 World Coordinate Mapping 26 2.3.3 Background subtraction 27 Chapter 3 Methodology 30 3.1 Layered Representative Body points 31 3.2 Layered Sector-based Shape Descriptor 40 3.3 Human Orientation and Orientation Refinement 44 3.3.1 Human Orientation 45 3.3.2 Layered Representative Body Points with Orientation Refinement 50 3.3.3 Layered Sector-based Shape Descriptor with Orientation Refinement 51 3.4 Classification 52 Chapter 4 Experiment 55 4.1 Environmental Description 55 4.2 Datasets Description 56 4.2.1 Top-View 3D Daily Activity Dataset 57 4.2.2 Top-View 3D Daily Activity with Orientation Dataset 59 4.3 Action Recognition Results 61 4.3.1 Top-View 3D Daily Activity Dataset 61 4.3.2 Top-View 3D Daily Activity with Orientation Dataset 64 Chapter 5 Conclusion and Future Work 70 REFERENCE 72 | |
dc.language.iso | en | |
dc.title | 利用分層俯視角深度特徵應用於日常活動辨識 | zh_TW |
dc.title | Daily Activity Recognition Using Features from Layered Top-View Depth Information | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳永耀(Yung-Yao Chen),洪一平(Yi-Ping Hung),陳祝嵩(Chu-Song Chen),范欽雄(Chin-Shyurng Fahn) | |
dc.subject.keyword | 活動辨識,俯視角,深度,動態時間校正, | zh_TW |
dc.subject.keyword | Top-view,activity recognition,depth,dynamic time warping, | en |
dc.relation.page | 75 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-08-18 | |
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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