基於手部骨架和深度信息的靜態手勢即時辨識研究與應用

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姓名

徐銜鴻(HSIEN HUNG HSU) 查詢紙本館藏

畢業系所

光機電工程研究所

論文名稱

基於手部骨架和深度信息的靜態手勢即時辨識研究與應用
(Research and Application of Real-Time Recognition of Static Hand Gesture Based on Information of Hand Skeleton and Depth)

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摘要(中)

本研究以開發一個即時辨識手勢系統為目的。以新興的Intel® RealSense 3D相機技術並結合支持向量機器(Support vector machine)來做資料分類達到即時辨識手勢。
在現今的科技發展手勢操作在各項產業中扮演著不可或缺的角色，其中在智慧型手機、平板電腦、個人電腦、筆記型電腦、電視等，都可能在未來的發展中置入具有深度學習功能的智慧型相機，並藉由手勢控制改變目前的人機互動體驗，因此本研究藉由Intel® RealSense 3D相機來探討手勢辨識研究。
本研究以Intel® RealSense 3D相機擷取手部關節點，擷取的手部關節點為22個含有世界三維座標的深度點，並藉由台灣大學林智仁教授所開發的LIBSVM將所擷取的手部關節點做訓練產生手勢模型，接下來將Intel® RealSense 3D相機擷取的手部關節點導入由LIBSVM生成的手勢模型做比較即可得到分類結果
使用本研究所開發的人機介面程式，可以辨識10種數字手勢且平均辨識率達99.5%，並將11個英文字母手勢應用於手勢打字，以驗證本研究之正確性。

摘要(英)

Gestures control technology is becoming more advanced. In the future, smart phones, Tablet, personal computers, laptops, TVs, etc., will utilize smart cameras with deep learning capabilities to recognize gestures. Using gesture control changes the human-computer interaction experience, so this study explored gesture recognition using Intel® RealSense 3D cameras, and the goal is to develop a Real-Time Recognition of Static Hand Gesture system.
The data for the recognition gestures is classified by using Intel® RealSense 3D camera technology combined with Support Vector Machine. In this study, hand joints were imported using Intel® RealSense 3D camera. The extracted 22 hand joints includes the world′s three-dimensional coordinates using LIBSVM developed by Professor Lin of Taiwan University, and the extracted hand joints are trained to generate a gesture model. The gesture taken by the Intel® RealSense 3D camera are compared to the LIBSVM generated gesture model to receive a classification result.
After implementing the human-computer program developed in this study, on average of 99.5% of numeric gestures were correctly identified, and 11 alphabets were recognized to conduct gesture typing.

關鍵字(中)

★ Intel® RealSense 3D相機
★ 深度點資料
★ 支持向量機器
★ LIBSVM
★ 手勢辨識

關鍵字(英)

★ Intel® RealSense 3D Camera
★ Depth Data
★ Support Vector Machine
★ LIBSVM
★ Gesture Recognition

論文目次

摘要I
AbstractII
致謝III
目錄IV
圖目錄VI
表目錄VIII
第一章緒論 1
1.1研究背景 1
1.2手勢辨識相關文獻回顧 2
1.2.1 以視覺為基礎之方法進行手勢辨識之研究 2
1.2.2 手勢辨識之類別討論 6
1.2.3 以深度資訊為基礎之靜態手勢辨識系統 7
1.3研究目的 11
1.4論文架構 12
第二章基礎理論 13
2.1 Intel® RealSense 3D相機 13
2.1.1 Intel® RealSense 3D相機坐標系統 16
2.2 Intel® RealSense Software Development Kit 手部追蹤組件 17
2.2.1手部追蹤組件追蹤模式介紹 18
2.2.2取得手部關節點資料 20
2.3手勢的特徵擷取 22
2.4 Support Vector Machine原理 25
2.4.1線性可分SVM 25
2.4.2非線性可分SVM 26
2.5 LIBSVM 27
2.6 小結 27
第三章系統架構 28
3.1使用IRS 3D相機與IRS SDK進行手部關節點的追蹤 28
3.1.1建立SenseManager介面 28
3.1.2使用PXCMHandData來獲得手部關節點資料 30
3.2使用LIBSVM進行手勢特徵的訓練與建立手勢模型流程 32
3.2.1使用LIBSVM之進行手勢特徵的訓練 32
3.2.2使用LIBSVM之建立手勢模型檔的建立 34
3.3使用LIBSVM進行手勢的辨識與分類流程 38
3.3.1人機介面程式使用LIBSVM工具包將手勢模型與程式結合 38
3.3.2使用LIBSVM進行手勢的辨識與分類 39
3.4人機介面程式與執行流程 41
3.4.1人機介面程式介面介紹 42
3.5.2人機介面程式執行流程 45
第四章實驗結果與討論 46
4.1實驗設備與系統環境 46
4.2數字手勢辨識實驗 47
4.2.1數字手勢辨識實驗流程 48
4.2.2標準數字手勢辨識實驗結果 49
4.3手指彎曲與辨識率的關係之手勢辨識實驗 54
4.3.1手指彎曲與辨識率實驗流程 58
4.3.2手指彎曲與辨識率實驗結果 59
4.4英文字母手勢 60
4.5手勢辨識之打字應用 65
4.5.1數字手勢之打字應用 65
4.5.2英文字母手勢之打字應用 69
第五章結論與未來展望 75
5.1結論 75
5.2未來展望 75
參考文獻. 77

參考文獻

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指導教授

李朱育(JU YI LEE)

審核日期

2018-1-29

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