基於智慧眼鏡之擴增實境輔助系統｜國立中央大學博碩士論文系統

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研究生：	張湘菱 Hsiang-Ling Chang
論文名稱：	基於智慧眼鏡之擴增實境輔助系統 A Smart-Glasses-based Augmented Reality Assisted System
指導教授：	蘇木春 Mu-Chun Su
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
論文出版年：	2018
畢業學年度：	106
語文別：	中文
論文頁數：	105
中文關鍵詞：	擴增實境、手勢偵測、手指指向分析
外文關鍵詞：	augmented reality, hand gesture recognition, finger-pointing analysis
相關次數：	點閱：23 下載：0
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近年來，擴增實境蓬勃發展，隨著智慧眼鏡的問世，更是讓這項技術為生活帶來許多的便利性。本論文提出一套擴增實境輔助系統，將系統架設於智慧眼鏡上，讓使用者得以透過個人視角使用此系統。
本系統功能包含（1）使用一套簡易流程建立裝置物件資料集，採用Mask R-CNN方法辨識裝置上的物件類別與位置（2）從影像中擷取指向手勢，並分析指向，依照手指指向顯示物件資訊（3）使用校準物件分析虛擬輔助工具該顯示的角度。
本系統的研發目的在於提供一套可輔助技術人員訓練之系統，藉由指向手勢，即可顯示操作人員欲了解的物件資訊。根據系統的實驗顯示，物件偵測的辨識率達到95.5%；手勢偵測的Kappa值為0.93，且平均偵測到手勢的秒數為0.26秒，即使在不同光線下，指向分析的準確率也有79%，由此可證明本論文所使用的方法具有很好的可信度。

Recently, the application of augmented reality has become more and more prevalent. With the advent of smart-glasses, the related research of augmented reality has been grown vigorously. Therefore, this dissertation proposes an augmented reality assisted system which will be set up on the smart-glasses. By setting the system on the smart glasses, the user will be able to use the system in personal perspective.
There are three main features in the system.（1）With a set of simple procedure, the system will set up dataset of objects on the device. Moreover, the system can identify the object and its position on the device automatically by using the Mask R-CNN method.（2）By capturing pointing gesture from the image and analyzing the pointing direction, the system will display the object information according to the finger pointing direction.（3）Using the calibrating object to analyze the rotation angles of virtual tools.
The aim of this system is to provide a system that can assist technicians in training. With the finger pointing, the system can show the object information which the user wants to know on the smart-glasses immediately. According to the results of the experiments, the percentage of recognition of object detection is 95.5%, the Kappa value of recognition of gesture detection is 0.93, and the average time for detecting pointing gesture is 0.26 seconds. Furthermore, even under different light, the proportion of accuracy of the pointing analysis is up to 79%. Based on the results of the experiments, it was proved that the method which was applied in this dissertation is applicable.

摘要    i
ABSTRACT    ii
致謝    iv
目錄    v
圖目錄    vii
表目錄    x
第一章、緒論    1
1-1 研究動機    1
1-2 研究目的    2
1-3 論文架構    3
第二章、相關研究    4
2-1 擴增實境應用    4
2-2 物件偵測技術    7
2-3 Mask R-CNN    12
2-4 手指指向分析技術    15
2-4-1 手勢偵測技術    15
2-4-2 手指指向分析    17
第三章、研究方法    19
3-1 系統架構    19
3-1-1 硬體介紹    20
3-1-2 系統流程    21
3-2 裝置物件偵測    22
3-3 手指指向分析演算法    24
3-3-1 指向手勢擷取    24
3-3-2 手指指向分析    29
3-4 虛擬工具定位及旋轉角度    36
3-4-1 虛擬工具定位    36
3-4-2 虛擬工具旋轉角度    37
第四章、實驗設計與結果    40
4-1 引擎室物件偵測實驗    40
4-1-1 實驗設計    40
4-1-2 實驗評估方式    42
4-1-3 實驗結果    42
4-1-4 實驗分析    43
4-2 主機板物件偵測實驗    46
4-2-1 實驗設計    46
4-2-2 實驗結果與分析    48
4-3 偵測手勢所需時間實驗    49
4-3-1 實驗設計    49
4-3-2 實驗評估方式    50
4-3-3 手勢辨識率與花費時間實驗結果    51
4-3-4 手勢偵測實驗結果分析    55
4-4 不同光線程度之手勢偵測所需時間實驗    56
4-4-1 實驗設計    56
4-4-2 手勢辨識率與花費時間實驗結果    58
4-4-3 手勢偵測實驗結果分析    62
4-5 手勢與非手勢偵測實驗    63
4-5-1 實驗設計    63
4-5-2 實驗評估指標    65
4-5-3 實驗結果與分析    67
4-6 手指指向分析實驗    70
4-6-1 實驗設計    70
4-6-2 指向手勢擷取實驗與分析    71
4-6-3 指向判斷實驗與分析    75
4-6-4 指向分析準確率實驗與分析    79
4-6-5 手指指向分析結論    83
4-7 旋轉角度實驗    84
第五章、結論與未來展望    85
5-1 結論    85
5-2 未來展望    86
參考文獻    87


                                

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