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研究生:王健宇
研究生(外文):Chien-Yu Wang
論文名稱:可變形樣板於即時人臉追蹤系統之應用
論文名稱(外文):Utilization Of Deformable Templates In Real-Time Face Tracking System
指導教授:趙健祥
指導教授(外文):Chien-Hsiang Chao
學位類別:碩士
校院名稱:國立中山大學
系所名稱:機械與機電工程學系研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:100
中文關鍵詞:即時影像追蹤人臉偵測影像處理模糊控制
外文關鍵詞:image processingfuzzy logicreal-time image trackingface detection
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數位影像處理發展已有時日,偵測與追蹤方面更是牽涉了許多數位技術。本文將從數位影像處理的觀點出發,論述基於影像處理的影像偵測概念,並基於影像矩形特徵在boosted cascade方面的研究成果,應用其方法發展一人臉偵測與追蹤系統。由於人臉偵測上常需要大量的運算,對於複雜環境下,影像變動量較大時往往無法有效率偵測追蹤,為了來提高效率,使用更多的矩形特徵並採用45度斜向快速影像積分的計算方式,結合可變形樣板可以旋轉目標影像的特性,計算部份影像區塊來減少計算量以改善系統性能,在PAN-TILT快速平台運動控制上,採用模糊控制器來追蹤移動中的物體。實驗測試後有不錯的結果,可應用於不同的需求。
The digital image processing has been developed for a long time. The image detection and tracking are involved to a variety of digital techniques. In this research we introduce the digital image processing techniques, base on a boosted cascade of simple features to develop a face detection and tracking system. Due to a large amount of computation in face detection under the complex environment will affect the detection rate and velocity efficiency. Therefore, we use the extended feature and set of 45゚ rotated feature with fast feature computation which called the integral image, combine with the deformable templates. We can compute a part of the image block to reduce the computation and improve the system. In the PAN-TILT unit, we use fuzzy logic. The results of experiment show that system is robust and fast.
目錄
誌謝 I
摘要 II
ABSTRACT III
目錄 IV
圖索引 VI
表索引 IX
符號索引 X
第一章 概論 1
1.1研究動機 1
1.2文獻回顧 2
1.3本文架構 6
第二章 影像處理原理 7
2.1數位影像 7
2.2色彩空間 9
2.3 頻率空間 16
2.4 邊緣檢測 17
2.5 移動物體偵測 19
2.6 物件辨識 20
第三章 人臉偵測技術 22
3.1影像積分 22
3.2 矩形特徵 27
3.3 ADABOOST訓練學習演算法 33
3.4串連式偵測器架構CASCADE 35
3.5 可變形樣板追蹤之應用 40
第四章 設備與控制 54
4.1 系統軟硬體設備 54
4.2 控制器 63
第五章 實驗與結果 71
5.1人臉偵測結果 71
5.2即時人臉追蹤實驗 76
第六章 結論與未來展望 79
參考文獻 81
圖索引
圖1 數位影像示意圖 8
圖2 RBG三色圖 10
圖3 CMY分色圖 11
圖4 色相環型圖 14
圖5 灰階化圖形 16
圖6 傅立葉頻譜轉換圖比較 17
圖7 Sobel運算圖 18
圖8 影像相減示意圖 19
圖9 快速積分概念圖 23
圖10 矩形總和面積表SAT(Summed Area Table) 23
圖11旋轉矩形總和面積表RSAT(Rotated Summed Area Table) 25
圖12 45度旋轉矩形計算結構圖 26
圖13 矩形框組成的Harr-Like特徵 27
圖14 矩形特徵與人臉偵測時所選擇的特徵 28
圖15 矩形視窗參數定義圖 29
圖16 人臉訓練部分範本圖 33
圖17 串聯式偵測器結構示意圖 36
圖18 偵測器架構圖 38
圖19 NIC法追蹤區塊影像追蹤流程 51
圖20 即時更新可變形樣板追蹤流程 53
圖21 硬體架構圖 54
圖22 CCD攝影機 55
圖23 M100 電視卡 56
圖24 PAN-TILT 平台 57
圖25 PTU 控制器 58
圖26 PTU 系統圖 58
圖27 光學鏡頭 59
圖28 RS-232 (DB9) 腳位與PTU控制器連接定義圖 60
圖29 數位影像相對位置關係圖 61
圖30 目標圖形誤差示意圖 62
圖31 模糊控制器歸屬函數 65
圖32 三角形歸屬函數與歸屬度圖 66
圖33 偵測器參數調整 71
圖33 (a) 1024x768 偵測結果1 72
圖33 (b) 1024x768 偵測結果2 73
圖34 640x480 圖片測試結果 73
圖35 320x240 圖片測試結果 74
圖36 (a) PAN 追跡效能 77
圖36 (b) TILT追蹤效能 77
圖36 (c) 系統計算的時間 78
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