跳到主要內容

臺灣博碩士論文加值系統

(44.211.117.197) 您好!臺灣時間:2024/05/27 05:46
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:黎陳秀
研究生(外文):Tran-Tu Le
論文名稱:以FPGA實現即時人臉追蹤系統
論文名稱(外文):FPGA Implementation of a Real-Time Face Tracking System
指導教授:徐元寶徐元寶引用關係
指導教授(外文):Yuan-Pao Hsu
學位類別:碩士
校院名稱:國立虎尾科技大學
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:2013
畢業學年度:102
語文別:英文
論文頁數:66
中文關鍵詞:FPGA人臉追蹤log-opponent
外文關鍵詞:FPGAface trackinglog-opponen
相關次數:
  • 被引用被引用:0
  • 點閱點閱:745
  • 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
本文提出了一種基於FPGA平台的即時人臉追蹤系统。在系统中,人臉的偵測主要是基於臉部的基本特徵:膚色。首先,圖像是由CMOS攝影機捕獲,接著經由子樣本模塊來減少圖像的複雜性。其次,將RGB格式的輸入圖像變換到對數色系座標系統(log-opponent)的顏色空間,並經由二進制膚色概率值的選定來過濾膚色像素。第三,本系統利用關連分量分析演算法(connected component analysis algorithm)來處理多皮膚區域的情況。第四,皮膚區域被順序地檢查以確定是否它們是人類臉部。最後,以綠色的方形框框住被偵測出的人臉以鎖定人臉,並在VGA螢幕上顯示。同時,控制安裝有CMOS攝影機的基座上的馬達以追蹤鎖定的人臉。實驗結果顯示,本系统具有良好的性能和满足人臉追蹤的目標。

This thesis presents a FPGA based platform for a real-time face detection and tracking system. In the system, human faces can be detected based on a particular facial feature, the skin color. Firstly, images are captured by a CMOS camera and a sub-sample module is used to reduce the images complexity. Secondly, the RGB format input image is transformed to log-opponent color space. A binary skin probability value is chosen to filter the skin pixels. Thirdly, a connected component analysis algorithm is applied to separate skin regions if the image has more than one skin regions. Fourthly, the skin regions are sequentially checked to determine if they are human faces or not. Finally, face tracking windows will be built to lock the human faces and displayed on the VGA screen. Meanwhile, a surveillance machinery is controlled, on which the CMOS camera is mounted, for tracking the prominent face in multi-angle. Experimental results show that the proposed system achieves high performance and satisfies the goal of real-time face tracking.


English Abstract i
Chinese Abstract ii
Acknowledgements iii
Table of Contents iiv
List of Tables vi
List of Equations vii
List of Figures viii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Structure of the Thesis 2
Chapter 2 Literature Review 3
2.1 Image-based Approaches 3
2.1.1 Principal component analysis 4
2.2.2 Neural network 5
2.2.2 Support Vector Machine 7
2.2 Feature-based Approaches 9
2.2.1 Edges 9
2.2.2 Skin colors 10
2.3 Summary of the Literature Review 14
Chapter 3 Face Detection and Tracking Design Methodology 15
3.1 Sub-sampling 16
3.2 Skin detection 18
3.3 Face detection and tracking 21
Chapter 4 Surveillance Machinery 28
4.1 Horizontal-Vertical Machinery 28
4.2 Camera Controlling Algorithm 34
Chapter 5 FPGA System Implementation 36
5.1 DE2-70 Board 36
5.2 System Architecture 40
Chapter 6 Experimental Results 47
Chapter 7 Conclusions and Future Work 55
7.1 Conclusions 55
7.2 Future Work 55
References 57
Extended Abstract 60
Curriculum Vitae 66




[1] S. Paschalakis, M. Bober, “Real-time face detection and tracking for mobile videoconferencing,” Real-Time Imaging, Volume 10, Issue 2, Pages 81-94, April 2004.
[2] E. Hjelmas, B. K. Low, “Face detection: a survey,” Computer Vision and Image Understanding, Volume 83, Issue 3, Pages 236-274, September 2001.
[3] P. M. Roth, M. Winter, “Survey of appearance-based methods for object recognition,” Technical Report, ICG-TR-01/08, Graz, January 2008.
[4] M. Turk, A. Pentland, “Eigenfaces for recognition”, Journal of Cognitive Neuroscience, Volume 3, Issue 1, Pages 71-86, 1991.
[5] H. A. Rowley, S. Baluja, T. Kanade, “Neural-network based face detection,” IEEE Transactions, PAMI, January 1998.
[6] C. Wang, L. Lan, Y. Zhang, M. Gu, “Face recognition based on principle component analysis and support vector machine,” International Workshop on Intelligent Systems and Applications (ISA), 2011.
[7] PCA-Principal Component Analysis, retrieved from:
http://phvuresearch.wordpress.com/2011/10/05/pca-principal-component-analysis/
[8] A. Jacquin, A. Eleftheriadis, “Automatic location tracking of faces and facial features in video sequences,” IEEE Proceedings of International Workshop on Automatic Face and Gesture Recognition, June 1995.
[9] Y. P. Hsu, H. C. Miao, C. Tsai, “FPGA implementation of a real-time image tracking system,” SICE Annual Conference 2010, August 2010.
[10] H. Wang, S. F. Chang, “A highly efficient system for automatic face region detection in mpeg video,” IEEE Transactions on Circuits and Systems for Video Technology, Pages 615-628, 1994.
[11] R. Gershon, A. D. Jepson, J. K. Tsotsos, “Ambient illumination and the determination of material changes,” Journal of Optical Society of America A, Volume 3, Page 1700, October 1986.
[12] M. Fleck, D. A. Forsyth, C. Bregler, “Finding naked people,” European Conference on Computer Vision, Volume 2, Pages 592-602, 1996.
[13] T. A. El-Hafeez, “A new system for extracting and detecting skin color regions from PDF documents,” International Journal on Computer Science and Engineering (IJCSE), Volume 2, Issue 9, Pages 2838-2846, 2010.
[14] D. A. Forsyth, M. Fleck, “Automatic detection of human nudes,” International Journal of Computer Vision, Volume 32, Issue 1, Pages 63-77, August 1999.
[15] M. Chung, I. Ko, “Obscene image detection algorithm using high-and low-quality image,” International Journal of Engineering and Industries, Volume 2, Issue 1, March 2011.
[16] M. J. Jones, J. M. Rehg, “Statistical color models with application to skin detection,” Computer Vision and Pattern Recognition, Volume 1, June 1999.
[17] T. Barbu, “An automatic face detection system for RGB images,” Journal of Computers, Communications and Control, Volume 6, Issue 1, Pages 21-32, March 2011.
[18] FPGA Pulse Width Modulation, retrieved from:
http://www.ece301.com/fpga-projects/53-pwm.html
[19] Servo control, retrieved from:
http://en.wikipedia.org/wiki/Servo_control
[20] DE2-70 User Manual. Altera Corporation.
[21] TRDB_D5M User Guide.
[22] W. N. Jian, C. S. Chieh, C. P. Jung, “A real-time multi-face detection implemented on FPGA,” International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2012), November 2012.
[23] C. Y. Chen, H. C. Huang, R. C. Hwang, “A low complexity real time face tracking system with fuzzy controller,” International Journal of System, Volume 8, Issue 4, December 2006.


QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top