智慧型公共安全監控系統之事件關係人臉部辨識｜國立臺灣科技大學博碩士論文系統

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研究生：	何彥智 Yen-Chih Ho
論文名稱：	智慧型公共安全監控系統之事件關係人臉部辨識 A Public Security Surveillance System for Face Detection and Recognition of Event Related Pedestrian
指導教授：	蔡超人 Chau-Ren Tsai
口試委員:	李祖添 none 蘇順豐 Shun-Feng Su 郭景明 Jing-Ming Guo 王乃堅 none
學位類別：	碩士 Master
系所名稱：	電資學院 - 電機工程系 Department of Electrical Engineering
論文出版年：	2012
畢業學年度：	100
語文別：	中文
論文頁數：	156
中文關鍵詞：	遺留物與遺失物偵測、數位信號處理器、臉部辨識
外文關鍵詞：	DSP, abandoned events and stolen events detection, face recognition
相關次數：	點閱：355 下載：6
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近幾年來隨著影像處理技術與數位信號處理器的進步，影像處理的技術已不僅僅只能局限於單張影像的處理。由於恐怖攻擊事件的頻傳與犯罪率的上升，影像監控技術已被廣泛運用在公共場所當中。本論文結合德州儀器TMS320DM642之數位信號處理器(DSP：Digital Signal Processor)開發模組以及雙攝影機架構，以實現具有臉部擷取以及人臉辨識功能的智慧型公共安全監控系統。在雙攝影機架構中，場景攝影機主要用來偵測場景中的遺留物事件與遺失物事件，而PTZ攝影機則用來追尋並擷取此事件關係人的臉部影像，而所擷取到的影像將與系統預先建立的人臉資料庫進行人臉辨識的比對。最後本系統將結合網路傳輸的架構以DSP為Server端，而遠端電腦為Client端，藉由網路傳輸，使用者即可以遠端來控制系統，以實現具有遠端監控能力的智慧型公共安全監控系統之事件關係人正面臉部影像擷取與辨識。

With the advances of image processing and digital signal processor, image processing technology has not only limited to dealing with single image. Because of the threat of terrorism and increasing crime recently, the visual surveillance systems are widely applied in public spaces. In the paper, we combine TMS320DM642 evaluation module and dual-camera module to achieve the public security surveillance system of face detection and face recognition. In dual-camera, the field camera is responsible for detecting the abandoned event and the stolen event. And the PTZ camera can be guided to track the owner of the event and capture faces. The faces would be recognized from face database which was established by the system. Finally, the system will associate with internet for the DSP platform as a Server-side, the remote PC as a Client-side. The user can remotely control the system, to achieve the public security surveillance system of face detection and face recognition.

摘    要	I
Abstract	II
致    謝	III
目    錄	IV
圖 索 引	VIII
表 索 引	XVI
第一章	緒論	1
1 研究動機與目的	1
2	研究方法	2
3	論文架構	3
第二章 系統架構	5
1	行人與物件分類程序	7
2	遺留物與遺失物分類程序	9
3	事件關係人追蹤程序	10
4	事件關係人正面臉部擷取與辨識程序	11
5	使用者介面傳輸程序	13
6	硬體規格與配置	15
第三章 行人物件偵測與分類	20
1	混合式前景區塊萃取	20
1.1	背景相減法	20
1.2	陰影濾除	23
1.3	雜訊濾除	25
1.4	使用碼本模型之前景萃取	28
1.5	基於差值原理之背景相減法	34
2	行人與物件	41
2.1	物件標記處理	41
2.2	行人與物件分類	43
2.3	關聯性區域比對	47
2.4	行人交錯之追蹤	48
第四章 	遺留事件與遺失事件	54
1	遺留物與遺失物分類	54
1.1	靜態目標物偵測	54
1.2	靜態目標物實際輪廓	56
1.3	邊緣輪廓色彩比對	58
2	遺留物持有者與遺失物擷取者追蹤	61
2.1	歷史影像與歷史影像吻合率	61
2.2	遺留事件與遺失事件關鍵性影像	66
2.3	遺留物持有者與遺失物擷取者追蹤	68
第五章	臉部資訊擷取	70
1	頭部追蹤參考點	70
2	PTZ攝影機之追蹤控制	72
2.1	雙攝影機座標轉換	72
2.2	PTZ攝影機倍率控制	78
3	正面臉部資訊擷取	79
3.1	眼睛對搜尋	80
3.2	正面臉部擷取	85
3.3	正面臉部影像大小正規化	86
第六章	人臉資料庫訓練與辨識	88
1	人臉資料庫訓練	88
2	人臉辨識	94
3	辨識結果	96
第七章	系統實現與效能測試	100
1	使用者介面	100
1.1	網路傳輸架構	101
1.2	使用者介面畫面配置	102
1.3	影像傳輸	103
1.4	辨識結果處理	107
1.5	追蹤遺留物件	110
2	系統軟體架構	112
3	系統實現	115
4	系統效能測試	122
第八章	結論	124
1	研究成果	124
2	未來發展方向	128
參 考 文 獻	129

                                

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