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dc.contributor.authorChen, Teng-Changen_US
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dc.description.abstract本論文提出一種即時三維人體姿態的預估方法,此方法藉由兩部攝影機擷取的人體輪廓資訊,在三維空間中定位人體的個別重要部位點,這些部位包含頭、身體的中心點、雙腳尖末端以及雙手末端點。首先,提出一種角度補償的色彩三原色分割演算法(AC-RGB segmentation),此法將人體與背景從一連串的影像中區分出來,並且,利用顏色夾角判斷初步擷取的部份,排除陰影的背景部份;最後經由一系列的影像處理,在兩部相異之攝影機畫面中,獲得個別完整的人體輪廓。對於個別攝影機影像上的二維人體姿態預估,則是藉由人體周圍輪廓之凸點以及幾何特徵來判斷。最後,經由二維人體姿態估測結果,使用重點體積交集法(SPVI)建構出三維人體的重要部位點。實驗結果顯示,角度補償的色彩三原色分割演算法(AC-RGB segmentation),相較於其他不同色彩模式的影像切割方法,有較好的切割結果。本論文也建構一個即時三維人體姿態預測系統,來驗證提出的方法是可行的。zh_TW
dc.description.abstractThis thesis proposes a real-time 3D human body posture estimation method to locate different significant body points in 3D space by using 2D extracted body contours from two cameras. The located 3D significant body points include the head, center of the body, tips of the feet, and tips of the hands. First, an Angle-Compensated RGB (AC-RGB) segmentation algorithm is proposed to distinguish the human body from background from a sequence of images in RGB model and reduce shadow influence by considering the included angle between pixels that are classified within a moving object and background. After segmentation, a sequence of image processing approaches then creates a complete contour of the human body for each of the two images from different cameras. Posture estimation in 2D is performed on each of the two images by using contour convex points and body geometrical characteristics. Finally, a Significant Point Volume Intersection (SPVI) method is proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed AC-RGB segmentation approach shows better performance than other segmentation approaches in different color models. This thesis also sets up a real-time 3D posture estimation system to verify the effectiveness of the proposed approach.en_US
dc.description.tableofcontentsChinese Abstract (摘要) i Abstract ii Content iii Table Content iv Figure Content v Chapter 1 Introduction 1 1.1 Survey 1 1.2 Organization of the Thesis 4 Chapter 2 Human Body Segmentation 5 2.1 Overview 5 2.2 RGB-based Moving Object Segmentation 7 2.3 Angle-Compensated RGB Model for Shadow Removal 14 2.4 Post Processing 17 Chapter 3 Two-Dimensional Posture Estimation 19 3.1 Overview 19 3.2 Features for Estimation 20 3.3 Posture Estimation Rules 25 Chapter 4 Three-Dimensional Posture Estimation 30 4.1 Overview 30 4.2 Camera Calibration 31 4.3 Locating 3D Significant Points 33 Chapter 5 Experiment 36 5.1 Camera Calibration 37 5.2 Angle-Compensated RGB Segmentation Results 39 5.3 2D and 3D Significant Points Locating Results 44 5.4 Real-Time 3D Experiments 63 Chapter 6 Conclusions 67 References 68zh_TW
dc.subjectimage segmentationen_US
dc.subjecthuman posture estimationen_US
dc.titleVision-based Real-Time 3D Human Posture Significant Points Estimationen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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