視訊監控物聯網架構的人臉辨識系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：20

、訪客IP：3.145.36.10

姓名

江仕傑(Shih-Jie Chiang) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

視訊監控物聯網架構的人臉辨識系統
(The Internet of Things Framework toward Face Recognition System)

相關論文

★ 整合GRAFCET虛擬機器的智慧型控制器開發平台	★ 分散式工業電子看板網路系統設計與實作
★ 設計與實作一個基於雙攝影機視覺系統的雙點觸控螢幕	★ 智慧型機器人的嵌入式計算平台
★ 一個即時移動物偵測與追蹤的嵌入式系統	★ 一個固態硬碟的多處理器架構與分散式控制演算法
★ 基於立體視覺手勢辨識的人機互動系統	★ 整合仿生智慧行為控制的機器人系統晶片設計
★ 嵌入式無線影像感測網路的設計與實作	★ 以雙核心處理器為基礎之車牌辨識系統
★ 基於立體視覺的連續三維手勢辨識	★ 微型、超低功耗無線感測網路控制器設計與硬體實作
★ 串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計	★ 一個快速立體視覺系統的嵌入式硬體設計
★ 即時連續影像接合系統設計與實作	★ 基於雙核心平台的嵌入式步態辨識系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

人臉辨識應用，經常都在高性能主機的伺服器上執行，但在雲端伺服器上，無法滿足即時性和可靠性需求。
本研究提出一個智慧型視訊監控物聯網架構，對此，應用裝置端是低硬體資源的攝影機，實作嵌入式人臉偵測；閘道器端是邊緣計算(edge computing)伺服器，執行深度學習人臉辨識。而雲端服務是一個Web界面，監控人機界面，使用者可由遠端，進行設定與監控目前影像狀況。
實驗驗證結果顯示，攝影機端能快速偵測，並擷取人臉影像，透過物聯網，傳至閘道器，執行深度學習，實現高效率人臉辨識。一來，滿足即時人臉辨識的需求，二來，減少網路頻寬的負擔。透過此研究，提高視訊監控物聯網系統整體效能，進而使物聯網時代快速推動。

摘要(英)

Face recognition application can execute in the server of high-performance mainframe constantly, yet it cannot meet the requirements of instantaneity and reliability in the cloud server.
This thesis aims to investigate the Internet of Things (IoT) framework toward intelligent video surveillance (IVS). The device end is a hardware-constrained camera embedding with face detection system whereas the gateway end is an edge computing server, executing deep learning face recognition. Moreover, cloud service is a web interface embedding with human machine interface, thus the user can remote the setting and monitoring for the current image status.
The findings of this research shows that the camera end can quickly detect and capture the face image to the gateway end in virtue of the IoT. It executes the deep learning and achieves high efficiency technology regarding face recognition. For one thing, IVS can meet the demand of real time face recognition. For the other, it can lighten the burden of network bandwidth. I believe that the findings from my thesis can elevate the overall effectiveness of IVS, and then open up a new era for the IoT.

關鍵字(中)

★ 物聯網
★ 人臉偵測
★ 邊緣計算
★ 人臉辨識
★ 深度學習

關鍵字(英)

★ Internet of Things
★ Face Detection
★ Edge Computing
★ Face Recognition
★ Deep Learning

論文目次

中文摘要……………………………………………………………………........i
英文摘要………………………………………………………………...ii
致謝………………………………………………………………..……...iii
目錄………………………………………………………………..……...ⅳ
圖目錄………………………………………………………………..…….........v
表目錄………………………………………………………………..……......viii
一、緒論…………………………………………………………….1
1-1 研究背景…………………………..………………………….….1
1-2 研究目的…………………………….……………………………...5
二、研究內容與方法……………………………….…………………….6
2-1卷積……………………..………………………………………7
2-2神經網路……………………………………………………….11
2-3卷積神經網路……………………………..……………….16
三、理論……………………………………………………………..20
3-1人臉辨識系統架構………………………………………………21
3-2人臉辨識系統架構模…………………………….................22
3-3影像積分功能模組………………………..…………............31
3-4 Adaboost功能模組…….……………...………………………33
3-5級聯分類器模組………………………..………………………35
3-6正規化功能模組………………………..………………………36
3-7人臉辨識功能模組………………….…………………………38
四、實驗部分…………………………..………………..............41
4-1實驗環境………………………………….……………………...41
4-2 Viola-Jones人臉偵測實驗…………….………………...44
4-3卷積神經網路人臉辨識實驗……………….…………….44
4-3-1訓練………………………………………………………………….44
4-3-2測試…………………………………..……………………………46
4-3-3評估………………………………….……………………………47
4-3-4計算效能評估……………………..…………………………...48
4-3-5應用……………………………….……………………………50
五、結論……………………………………….………………………51
5-1結論………………………………..……………………….…51
5-2 未來研究與方向……………………..…………………………52
參考文獻………………………………………………….……...………...53

參考文獻

[1] G. Lorraine, F. Nacerodien, “An Assessment of Closed Circuit Television Surveillance with Referen” IEE Proceedings - Vision, Image and Signal Processing, vol. 152, Issue 5, pp 192-204, 09 May 2005.
[2] Eric D. Daniel ,C. Denis Mee , Mark H. Clark, " Digital Video Recording, " in Magnetic Recording :The First 100 Years ,1, Wiley-IEEE Press,ch14 ,pp. 201-220, 1999.
[3] R.B. Hawkins, "The network is the video camera-technologies for enabling embedded video in intelligent networks", Distributed Imaging (Ref. No. 1999/109), IEE European Workshop, 18-18 Nov. 1999.
[4] Open Network Video Interface Forum., Open Network Video Interface Forum. ONVIF Core Specification Ver.2.1.1, 2012[Online]. Available: http://www.onvif.org/specs/core/ONVIF-Core-Specification-v211.pdf
[5] Open Network Video Interface forum., Open Network Video Interface Forum Core Specification Ver.2.2.1, 2012 [Online]. Available: http://www.onvif.org/specs/core/ONVIF-Core-Specification-v221.pdf
[6] Physical Security Interoperability Alliance. Physical Security Interoperability Alliance System Protocol Ver.1.2., 2012 [Online]. Available: http://www.psialliance.org/register_form.html?file=PSIA-Service-Model_v1_2dFinal.pdf
[7] MA Mei, TANG Na,” Integration application scheme of video surveillance and environmental monitoring system in grid”, Electronic Design Engineering, vol.22, no.9, May2015.
[8] G. Medioni, I. Cohen, F. Bremond, S. Hongeng and R. Nevatia,” Event detection and analysis from video streams”, IEEE transations on Pattern Analysis and Machine Intelligence, vol. 23, no.8, pp 873-889, 2001.
[9] Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang. “Deep learning face representation by joint identification-verification”, In Proc. of the Advance in Neural Information Processing Systems (NIPS), pp1988-1996, 2014.
[10] Jingen Liu, Saad Ali, Mubarak Shah,” Recognizing Human Actions Using Multiple Features”, Computer Vision and Pattern Recgnition, “2008.CVPR, IEEE Conferemce on”, pp1-8, June 2008.
[11] Andreas Lanitis, Christopher J. Taylor, and Timothy F. Cootes, "Automatic face identification system using flexible appearance models," Image and vision computing, vol. 13, no.5, pp. 393-401, 1995.
[12] L. Acasandrei, and A. Barriga, "AMBA bus hardware accelerator IP for Viola-Jones face detection", IET Computers & Digital Techniques, vol. 7, no. 5, pp. 200-209, 2013.
[13] L. Essannouni, and D. Aboutajdine, "Correlation of robust Haar-like feature", Electronics Letters, vol. 47, no. 17, pp. 961-962, 2011.
[14] S. Wu, and H. Nagahashi, "Parameterized AdaBoost: Introducing a Parameter to Speed Up the Training of Real AdaBoost", IEEE Signal Processing Letters, vol. 21, no. 6, pp. 687-691, 2014.
[15] L. Bruzzone, and R. Cossu, "A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps", IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 9, pp. 1984-1996, 2002.
[16] M.A. Turk and A.P. Pentland, "Face recognition using eigenfaces," presented at Computer Vision and Pattern Recognition, 1991.
[17] P.N Belhumeur, J.P. Hespanha, and D.J. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class specific linear projection," IEEE Transactions, Pattern Analysis and Machine Intelligence, 1997.
[18] M. Kirby and L. Sirovich,” Application of the Karhunen-Loeve procedure for the characterization of human faces”. IEEE Trans. Patt. Anal. Mach. Intell. 12, 1990.
[19] Z. Pan, R. Adams, and H. Bolouri, “Dimensionality reduction of face images using discrete cosine transforms for recognition.” submitted to IEEE Conference on Computer Vision and Pattern Recognition, 2000.
[20] J. Zhu, M.I. Vai and P.U. Mak, “Face Recognition Using 2D DCT with PCA", in the 4nd Chinese Conference on Biometric Recognition (Sinobiometrics’03) at Beijing, P. R. China, Dec. 7-8, 2003.
[21] G. Guo, S.Z. Li and K. Chan, “Face Recognition by Support Vector Machines”, Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000, pp. 196.
[22] M. Safari, M.T. Harandi and B.N. Araabi, “A SVM-based method for face recognition using a wavelet PCA representation of faces”, Image Processing. ICIP ′04. 2004 International Conference on, vol. 2, pp.853 - 856, 24-27 Oct. 2004.
[23] V.V. Kohir and U.B. Desai, “Face recognition using a DCT-HMM approach,” in Proc. IEEE Workshop on Applications of Computer Vision (WACV’98), Princeton, NJ, pp.226–231,1998.
[24] Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, and Andrew D. Back,” Face Recognition: A Convolutional Neural-Network Approach”, IEEE Transactions on Neural Networks, vol. 8, no. 1, pp.98-113, January 1997.
[25] Lawrence, S., C. L. Giles, A. C. Tsoi, and A. D. Back, "Face recognition: a convolutional neural-network approach," IEEE Trans. on Neural Networks, vol.8, no.1, pp.98-113, 1997.
[26] Warren S. McCulloch, Walter Pitts, "A logical calculus of the ideas immanent in nervous activity, " The bulletin of mathematical biophysics, vol. 5, Issue. 4, pp 115–133, December 1943.
[27] 曾柏耀, "嵌入式人臉偵測系統設計與實作," 國立中央大學資訊工程學系碩士論文, 2016.
[28] O, Peng, "A Fast-Integral Image Computing Hardware Architecture with High Power and Area Efficiency", IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 62, no. 1, pp. 75-79, 2014.
[29] LuigiDi Stefano , StefanoMattoccia and FedericoTombari ,” ZNCC-based template matching using bounded partial correlation”, Pattern Recognition Letters,Vol.26,pp.2129-2134, October 2005.
[30] R.W. Frischholz, U. Dieckmann,” BiolD: a multimodal biometric identification system”, Computer , vol.33,issue 2,Feb 2000.
[31] Terence Sim, Simon Baker, and Maan Bsat,” The CMU Pose, Illumination, and Expression (PIE) Database”, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, May2002
[32] T. Ojala, M. Pietikainen, T. Maenpaa,” Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence,vol .24,issue 7,Jul 2002.

指導教授

陳慶瀚

審核日期

2017-11-27

推文