跳到主要內容

臺灣博碩士論文加值系統

(35.175.191.36) 您好!臺灣時間:2021/07/30 18:04
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:陳志銘
研究生(外文):Chih-Ming Chen
論文名稱:利用環場及PTZ攝影機建構室內環境監控系統作臉部辨識
論文名稱(外文):The Using of Omni and PTZ Cameras in Constructing In-door Surveillance System for Face Recognition
指導教授:范國清范國清引用關係
指導教授(外文):Kuo-Chin Fan
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程研究所
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:2004
畢業學年度:92
語文別:中文
論文頁數:56
中文關鍵詞:臉部正面判定環場攝影機
外文關鍵詞:omni-cameradeciding of face orientation
相關次數:
  • 被引用被引用:6
  • 點閱點閱:109
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
智慧型監控系統一直是近年來相當熱門的研究項目,其目的大都是為了達到目標物之偵測與追蹤為主。監控系統依照環境不同可分成室內及室外兩種,室外監控系統其重點大都著重在行為分析上,而室內監控除了行為分析,有時還加上了身份辨識的目的。
大部分的室內環境監控系統,大都只用一台或多台相同的攝影機來取像,在此種情況下有時並無法發揮其最大的功效,本研究利用兩種不同功能的攝影機:環場攝影機(omni-camera)及PTZ攝影機建構室內環境監控系統,利用兩種攝影機不同特性的優缺點作互補,以期達到最好的效果。
在本篇論文中,並未使用到許多相當複雜的算式或演算法,大多利用基本的影像處理方法來達到監控的目的。在前景物偵測部分,利用對環場攝影機之影像作背景相減來找到目標物位置並持續追蹤,而背景建立的方法則使用漸進式的背景影像建構法,以期能快速建立並更新背景影像。在臉部正面判定部份,則是利用膚色資訊及幾何條件在PTZ攝影機之影像中尋找人臉位置,再利用人臉特徵中五官在臉部區域之相對位置判斷出人臉方向。實驗結果顯示本系統對於以臉部辨識為目的之室內監控系統有相當的可行性。
The development of unsupervised surveillance systems always attracts the attention of many researchers due to its importance in several applications. The main purpose of most surveillance systems is to detect and track the target. It can be classified into two categories according to the environment, which are indoor and outdoor surveillance systems. Most of outdoor surveillance systems focus on behavior analysis, whereas indoor surveillance systems emphasize not only on behavior analysis but also on identity recognition.
Almost all indoor surveillance systems only use one kind of cameras to capture the images. However, the performance will not be optimal under this constraint. In this thesis, two different kinds of cameras, called omni-camera and PTZ camera, are utilized to build our indoor surveillance system. These two kinds of cameras can be the complementary of each other to accomplish the task in reaching the best performance.
In this thesis, basic image processing techniques are developed to accomplish the surveillance goal. To accomplish the task of detection and tracking, we apply background subtraction method on the images captured from omni-camera to detect the target and track it continually. Progressive method is employed in this part to generate background image with an eye to generating and updating background image quickly. As to the deciding of face orientation, skin color information and the relating geometric features are firstly utilized to find all faces presented in the images captured from PTZ camera. Then, the relative positions of all facial characteristics are used to determine the orientation of faces. Experimental results demonstrate that our devised system is effective in indoor surveillance systems for face recognition purpose.
Abstract…….………………………………………………………………………..Ⅰ
摘要.......………………………………………....................…………Ⅱ
目錄…………………………………………..………………………….…………...Ⅳ
圖形目錄…………………………………..…………………………………………Ⅵ
第一章 緒論……………………………………………………………..…….…….1
1.1 研究動機………………………………….....……………………………1
1.2 相關研究………………………….………………………………..……..2
1.2.1 視訊監控………………………………………………………..…2
1.2.2 臉部偵測…………………………………………………………..3
1.3 系統簡介……………………………….…………………………………4
1.4 論文架構………………………………….…………………………...….4
第二章 相機特性及影像處理技術………………………………………………....6
2.1 相機特性介紹…………………..…………….…………………………..6
2.1.1 環場攝影機(omni camera)………………………………………6
2.1.2 PTZ攝影機……………………………………………………….7
2.2 影像處理技術………………………………….…………………………8
2.2.1 二值化…………………….…...……………………………....…..8
2.2.2 型態學的介紹……………………………………………….…….9
2.2.3 連通元件…………………………………………………………11
第三章 前景物偵測及追蹤………………………………………………………..14
3.1 背景的建立…………………….………………………………………..15
3.2 前景物偵測及追蹤……………………….…………………………..…17
3.2.1 前景物偵測……………...……….…………………………..…..17
3.2.2 前景物追蹤…………….…..…………….………………………19
3.3 PTZ攝影機旋轉角度計算…………….………………………….……21
第四章 人臉偵測及正面判定………………………………………………….….23
4.1 人臉偵測………………………………….………………………….….23
4.1.1 色彩空間…………………………………………………………23
4.1.2 膚色區域偵測……………………………………………………25
4.1.3 臉部區域判斷……………………………………………...…….27
4.2 臉部特徵偵測………………………………………………….……….29
4.2.1 眼睛定位……………………………………………………...….30
4.2.2 嘴唇定位……………………………………………………...….32
4.3 人臉正面判定……………...…………..……………………………....33
第五章 實驗結果…………………………………………………………….…….35
5.1 背景建立……………………………………………………………..…35
5.2 前景物追蹤……………………………………………………….…….38
5.3 臉部正面判定………………………………………………………..…39
5.4 全系統結合………………………………………………………….….41
第六章 結論與未來方向………………………………………………………..…44
6.1 結論………………………………………………………………..……44
6.2 未來方向……………………………………………………………..…44
參考文獻……………………………………………………………………………..46
[1] M. Greiffenhagen, D. Comaniciu, H. Neimann and V. Ramesh. “ Design, Analysis, and Engineering of Video Monitoring Systems: An Approach and a Case Study,” in Proceedings of IEEE, vol.89, no.10, October, pp.1498-1517, 2001.

[2] Q. Liu, D. Kimber, L. Wilcox, M. Cooper, J. Foote and J. Boreczky. “ Managing a Camera System to Serve Different Video Request,” in Proceedings of IEEE International Conference on Multimedia and Expo, ICME ’02, vol. 2, pp. 13-16, Aug, 2002.

[3] T. Mituyosi, Y. Yagi and M. Yachida. “ Real-time Human Feature Acquisition and Human Tracking by Omnidirectional Image Sensor,” in Proceedings of IEEE Conference on Multisensor Fusion and Integration for Intelligent System, pp. 258-263, 2003

[4] S. Morita, K. Yamazawa and N. Yokoya. “ Networked Video Surveillance Using Multiple Omnidirectional Camera,” in Proceedings of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 1245-1250, Kobe, Japan, 2003.

[5] X. Chen and J. Yang. “ Towards Monitoring Human Activities Using an Omnidirectional Camera,” in Proceedings of the Fourth IEEE International Conference on Multimodal Interfaces (ICMI’02), pp.423-428, October. 2002

[6] J. W. Lee, S. You, and U. Neumann, “ Tracking with Omni-directional Vision for Outdoor AR Systems,” in Proceedings of the International Symposium on Mixed and Augmented Reality (ISMAR’02), pp.47-56, 2002.

[7] S. L. Phung, A. Bouzerdoum, and D. Chai. “A Novel Skin Color Model in YCbCr Color Space and Its Application to Human Face Detection,” in Proceedings of the International Conference on Image Processing, vol.1, pp. 289 – 292, 2002.

[8] Y. Mitsukura, K. Mitsukura, M. Fukumi, N. Akamatsu, and S. Omatu. “ Robust Face Detection for Direction Changing Using Evolutionary Algorithms,” in Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, vol.2, pp. 870-873, Kobe, Japan, 2003.

[9] H. T. Quan, M. Meguro, and M. Kaneko, “ Skin-Color Extraction in Images with Complex Background and Varying Illumination,” in Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision (WACV’02), pp.280-285, December, 2002.

[10] K. W. Wong, K. M. Lam, and W. C. Siu. “ A Robust Algorithm for Detection of Human Faces in Color Images,” in Proceedings of ISCP’02, vol.2 pp. 1112-1115, August, 2002.

[11] Y. ARAKI, N. SHIMADA, and Y. SHIRAI, “ Detection of Faces of Various Directions in Complex Backgrounds, ” in Proceedings of the 16th IEEE International Conference on Pattern Recognition, vol. 1, pp. 409-412, August, 2002.

[12] Z. F. LIU, Z. S. You, A. K. Jain, and Y. Q. Wang, “ Face Detection And Facial Feature Extraction in Color Image,” in Proceedings of the Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’03), pp.126-130, September, 2003.

[13] Y. Ma, and X. Ding, “Face Detection Based on Hierarchical Support Vector Machines.” in Proceedings of the 16th IEEE International Conference on Pattern Recognition, vol. 1, pp. 222-225, August, 2002

[14] H. Ai, L. Ying, and G. Xu. “ A Subspace Approach to Face Detection with Support Vector Machines.” in Proceedings of the 16th IEEE International Conference on Pattern Recognition, vol.1 pp.222-225, August, 2002.

[15] W. Widjojo, and K. C. Yow, “ A Color and Feature-Based Approach to Human Face Detection.” in Proceedings of the Seventh International Conference on Control, Automation, Robotics and Vision (ICARCV’02), pp. 508-513, December, Singapore, 2002.

[16] C. Garcia, and G. Tziritas. “ Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis.” in IEEE Transactions on Multimedia, vol. 1. no. 3. September, pp. 264-277, 1999.

[17] Y. C. Chung, J. M. Wang, and S. W. Chen, “ Progressive Background Image Generation.” in Proceedings of 15th IPPR Conf. on Computer Vision, Graphics and Image Processing, pp. 858-865, 2002.

[18] H. Hongo, A. Murata, and K. Yamamoto. “ Consumer Products User Interface Using Face and Eye Orientation,” in Proceedings of 1997 IEEE International Symposium on Consumer Electronics, pp.87-90, 1997.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top