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研究生:洪耀宗
研究生(外文):Hung Yao Tsung
論文名稱:人臉即時偵測及追蹤系統
論文名稱(外文):A Real-Time Human Face Detection and Tacking System
指導教授:李建德李建德引用關係
指導教授(外文):Lee Jiann Der
學位類別:碩士
校院名稱:長庚大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:56
中文關鍵詞:人臉偵測人臉追蹤
外文關鍵詞:Face Detection、Face Tracking
相關次數:
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為了提供民眾的公共安全,一般的公共場合都有架設錄影監控系統來紀錄人們的舉動,一旦有需要時便能透過畫面來找出可疑人物。不過由於場景中的光線變化與人數多寡皆會影響監控品質,導致無法以肉眼去進行分析,因此還得經過影像處理來改善。光補償技術與人臉偵測法,可用來即時偵測並追蹤場景中的人臉。首先利用膚色分割與區域成長得到初步人臉區塊,再藉由色彩分析與形態學找出嘴唇與眼睛位置,最後運用本文提出的人臉結構和幾何關係做人臉分數評比;而搭配Pan-Tilt旋轉平台,配合景深資訊,使得台上之攝影機能夠即時隨著人臉移動,達到追蹤效果。由實驗結果顯示,本系統在一般環境下,嘴與眼睛的偵測率最高分別為96%與88%,而人臉偵測率為94%,證明本系統能正確的找出人臉位置。此亦驗證了本文所提演算法可有效完成人臉追蹤的目標。
For public safety issues recently, the video surveillance systems have been widely used to record and monitor people’s behaviors in public territories. If there is an emergent event, the possible information can be investigated immediately. In a practical application, human are detected first in the surveillance system, but the variant light resources in the scene influence the monitoring quality. To assist a practical video surveillance system, a human face detection system with multi image techniques is proposed in this study. Light compensation, the skin color segmentation, and the region growing methods are performed to obtain the initial face candidates first. Then the lip and eye locations are detected by the color and morphology information. Finally, the face structure and geometric relation are employed to calculate the facial score which benefits incomplete face situations. After the faces are detected, the camera on the Pan-Tilt platform is able to rotate to display the face in the center of the screen immediately. Furthermore, the deep information is used in the camera control to make this system more diversified. From experiment results under normal environmental conditions, the highest detection rates of mouth, eyes and face are 96%, 88% and 94% respectively. This implies that this proposed system has the ability to achieve the face-tracking goal.
第一章 緒論………………………………………………………………….1
1.1 研究動機…………………………………………………………….1
1.2 文獻回顧…………………………………………………………….1
1.3 系統流程…………………………………………………………….3
1.4 論文架構…………………………………………………………….5
第二章 人臉偵測…………………………………………………………….6
2.1 光線補償…………………………………………………………….6
2.2 色彩空間轉換……………………………………………………….7
2.3 型態學……………………………………………………………….9
2.3.1 二元型態學…………………………………………………..9
2.3.2 灰階型態學………………………………………………….11
2.4 膚色分割……………………………………………………………12
2.4.1 非線性YCbCr轉換………………………………………….12
2.4.2 區域成長法………………………………………………….14
2.5 臉部特徵偵測………………………………………………………16
2.5.1 嘴唇特徵…………………………………………………….17
2.5.2 人臉區塊更新……………………………………………….18
2.5.3 眼睛特徵…………………………………………………….19
2.6 可靠度計算…………………………………………………………20
第三章 景深測量…………………………………………………………….25
3.1 相機參數校正………………………………………………………25
3.1.1 座標系統…………………………………………………….25
3.1.2 數學模型…………………………………………………….26
3.1.3 外部參數求法……………………………………………….28
3.1.4 內部參數求法……………………………………………….30
3.2 二維影像的景深量測………………………………………………30
3.2.1 成像模型…………………………………………………….30
第四章 可程式控制旋轉台………………………………………………….33
4.1 旋轉台控制…………………………………………………………33
4.1.1 位置控制…………………………………………………….33
4.1.2 速度設定…………………………………………………….34
4.2 物體追蹤……………………………………………………………35
第五章 實驗結果與討論…………………………………………………….37
5.1 實驗環境……………………………………………………………38
5.2 人臉偵測實驗………………………………………………………39
5.2.1 實驗一……………………………………………………….40
5.2.2 實驗二……………………………………………………….42
5.2.3 實驗三……………………………………………………….46
5.2.4 實驗四……………………………………………………….47
5.2.5 實驗五……………………………………………………….50
第六章 結論及未來研究方向……………………………………………….53
參考文獻……………………………………………………………………...54
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