以軌跡辨識為基礎之手勢辨識系統

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姓名

洪兆欣(Chao-Hsin Hung) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以軌跡辨識為基礎之手勢辨識系統
(A Trajectory-based Approach to Gesture Recognition)

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摘要(中)

手勢辨識系統可廣泛應用於人機介面設計、醫療復健、虛擬實境、數位藝術創作與遊戲設計等領域，尤其是手語辨識系統，更是需要搭配準確且可行的手勢辨識系統。
本論文提出SOMART演算法，將動態手勢辨識之問題轉換為軌跡辨識問題處理。SOMART演算法主要包含兩個步驟，首先，將多維的手勢資訊利用SOM網路作基本手形分類器並投影至二維平面中。接著，將前一步驟所產生的平面軌跡輸入至改良後的ART網路做圖樣的辨識以辨識動態手勢。另外，我們利用「以軌跡辨識為基礎」的辨識概念，進行手部移動軌跡辨識，同樣可解決動態時序資料辨識的問題。
結果驗證部份，我們定義47種靜態手勢、103種動態手勢及八種手部移動軌跡，分別請十位使用者錄製手部移動軌跡資料，整體資料庫的數量為4650筆資料。靜態手勢的平均辨識率為92%，動態手勢的平均辨識率為88%。而手部移動軌跡的平均辨識率達99%。

摘要(英)

Gesture recognition is needed for a variety of applications such as human-computer interfaces, communication aids for the deaf, etc. In this thesis, we present a SOMART system for the recognition of hand gestures. The sequence of a hand gesture is first projected into a 2-dimensional trajectory on a self-organizing feature map (SOM). Then the problem of recognizing hand gestures is transformed to the problem of recognizing hand-written characters. The adaptive resonance theory (ART) algorithm generates multiple templates for each hand gesture. Finally, an unknown gesture is classified to be the gesture with the maximum similarity in the vocabulary via a template matching technique. In addition, the conception of SOMART system can also apply to hand movement trajectory recognition.
A database consisted of 47 static hand gestures, 103 dynamic hand gestures, and eight movement trajectories was tested to demonstrate the performance of the proposed method. The average recognition rate of static hand gestures is 92%, the recognition rate of dynamic hand gestures is 88%, and 99% for hand movement trajectories.

關鍵字(中)

★ 動態手勢辨識
★ 手語辨識
★ 自我組織特徵映射圖網路
★ 適應共振理論

關鍵字(英)

★ adaptive resonance theory (ART)
★ self-organizing feature maps (SOM)
★ dynamic gesture recognition
★ character recognition
★ sign language recognition

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 IX
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
第二章相關研究介紹 4
2.1 手勢定義 4
2.2 輸入元件介紹 5
2.2.1 手勢辨識輸入元件介紹 5
2.2.2 手部移動軌跡辨識輸入元件介紹 6
2.3 手勢相關研究 7
2.4 以軌跡辨識為基礎之辨識方法 10
第三章硬體設計 12
3.1 資料手套介紹 12
3.2 資料手套設計 14
3.2.1 設計重點 14
3.2.2 元件介紹 15
3.2.3 電路設計 16
第四章辨識方法及步驟 19
4.1 SOMART手勢辨識系統架構 19
4.2 靜態手勢辨識 20
4.2.1 自我組織特徵映射圖網路（SOM）演算法介紹 20
4.2.2 以SOM為基礎之靜態手勢辨識 21
4.2.2.1 參數設定 21
4.2.2.2 進行訓練 23
4.3 動態手勢辨識 29
4.3.1 適應共振理論（ART）演算法介紹 29
4.3.2 以ART演算法為基礎之動態手勢辨識 32
4.3.2.1 產生軌跡樣本 32
4.3.2.2 改良部分 33
4.3.2.3 訓練階段 35
4.3.2.4 動態手勢辨識 37
4.4 SOMART演算法 38
4.5 移動軌跡辨識 39
4.5.1 資料擷取 39
4.5.2 前處理 40
4.5.2.1 平滑化 40
4.5.2.2 維度縮減 41
4.5.3 移動軌跡辨識 42
4.5.3.1 定義基本方向向量與移動軌跡 42
4.5.3.2 定義二維特徵圖（2D Map） 44
4.5.3.3 進行辨識 44
第五章實驗結果 47
5.1 靜態手勢辨識結果 47
5.2 動態手勢辨識結果 52
5.3 手部移動軌跡辨識結果 55
第六章結論與未來展望 56
6.1 結論 56
6.2 未來展望 57
參考文獻 58

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指導教授

蘇木春(Mu-Chun Su)

審核日期

2006-7-22

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