基於深度資訊之手勢辨識｜國立臺灣科技大學博碩士論文系統

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研究生：	陳帷綸 Wei-Lun Chen
論文名稱：	基於深度資訊之手勢辨識 Depth-based Hand Gesture Recognition Using Hand Movements And Defects
指導教授：	林昌鴻 Chang-Hong Lin
口試委員:	林淵翔 Yuan-Hsiang Lin 沈中安 Chung-An Shen 阮聖彰 Shanq-Jang Ruan
學位類別：	碩士 Master
系所名稱：	電資學院 - 電子工程系 Department of Electronic and Computer Engineering
論文出版年：	2015
畢業學年度：	103
語文別：	英文
論文頁數：	67
中文關鍵詞：	深度攝影機、動態手勢辨識
外文關鍵詞：	Depth Cameras, Dynamic Hand Gesture Recognition
相關次數：	點閱：262 下載：5
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手勢辨識領域的研究已經發展多年，而近來由於智慧型運算裝置的普及，人機介面已成為相當重要的研究領域，手勢辨識是與機器溝通相當直覺且便利的方式，因此利用手勢辨識來控制裝置是相當好的方法。由於低成本的深度攝影機的普及與發展，深度攝影機已經可以被廣泛運用在消費性電子上。在此論文中，我們提出了一個只用深度資訊來辨識動態手勢的手勢辨識系統，而這個系統可以辨識十二種不同的動態手勢，其中包含上揮、下揮、左揮、右揮、轉圈、揮手、縮放、推、拖曳、旋轉…等手勢。首先我們將背景濾掉(Background subtraction)，以去除不必要的資訊，並且可以得到主要使用者的深度資訊。此外我們可以追蹤手心的位置座標，並且用自適性方框取得手掌區域的深度資訊，當取得手掌區域後，我們藉由手掌深度資訊計算手部參數，此系統利用這些手部參數來辨識動態手勢。在實驗中我們透過兩個人於兩個不同的深度來驗證系統，並且左手與右手都分別驗證，實驗結果顯示此動態手勢辨識系統之平均辨識率為92.95%。

The hand gesture recognition has a long history within the computer vision community and is one natural and intuitional way to communicate with human and machine. Since low-cost depth cameras have been launched, depth cameras become more and more affordable in consumer electronics. In this thesis, we proposed a dynamic hand gesture recognition system by using only the depth information. The proposed system can recognize twelve different dynamic hand gestures, including swipes, scales, push, wave, rotates, circle, and drag. First, the background subtraction is used to remove the unnecessary information, and the depth information of main user can be obtained. Furthermore, hand position can be tracked, and the region of hand is extracted as an adaptive square. Once the region of hand is obtained, the hand parameters are obtained by calculating the depth information of hand region. The proposed system can recognize dynamic hand gesture by using the hand parameters. In the experiment, the performance of the proposed system is verified by two different people at 2 different depths, and both right and left hands are verified. The experimental result show that the proposed system can recognize the dynamic hand gestures with an average recognition rate of 92.95%.

中文摘要..................................................i
Abstract................................................ii
致謝.....................................................iii
List of Contents........................................iv
List of Figures.........................................vi
List of Tables..........................................viii
Introduction..........................................1
1 Motivation..........................................1
2 Contributions.......................................2
3 Thesis Organization................................. 2
Related works............................................3
1 Static hand posture recognition......................3
2 Dynamic hand gesture recognition....................9
Proposed methods......................................14
1 Preprocessing........................................15
1.1 Depth image acquirement............................15
1.2 Back ground subtraction...........................16
1.3 Hand detection and tracking........................16
2 Hand palm parameter calculation.....................17
2.1 Hand palm region extraction........................17
2.2 Hand palm contour extraction........................20
2.3 Defect and fingertips detection....................21
3 Dynamic gesture recognition.........................24
3.1 Hand trajectory base gestures......................25
3.1.1 swipe, push.....................................26
3.1.2 Wave............................................32
3.1.3 Circle..........................................35
3.2 Fingertip behavior based gestures..................39
3.2.1 Rotate..........................................39
3.2.2 Scale...........................................45
3.2.3 Drag.............................................46
Experimental results...................................50
1 Developing Platform..................................50
2 Experiment results of dynamic gesture recognition....51
2.1 Experiment results of the hand trajectory based gestures.51
2.2 Experiment results of the fingertip behavior
based gestures..........................................57
3 Analysis of proposed method..........................60
3.1 Dynamic gesture recognition analysis..............60
Conclusions and future works..........................65
References..............................................        66

                                

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全文公開日期 2018/08/10 (校內網路)
全文公開日期本全文未授權公開 (校外網路)
全文公開日期本全文未授權公開 (國家圖書館：臺灣博碩士論文系統)

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