由於人們對於人身財產及居家安全的要求，視訊監控的需求越來越多。但是傳統的視訊監視只能錄下影片，無法針對影像資訊進行即時分析和決策。因此無需人員監看，能夠自動分析影像並提供決策資訊的智慧型視訊監控系統越來越受到大家的關注。本論文因此提出了一個以人類行走步態做為生物特徵的身份辨識系統，針對視訊監控應用，可提供即時影像分析並辨識目標物的身份。
本研究的步態辨識流程分為步態影像切割、步態特徵抽取、以及機率神經網路分類器的步態辨識。步態影像切割需對連續變動的場景建立背景模型，並進行影像相減以得到前景物。特徵抽取的方法則是對步態影像進行水平投影與垂直投影，並將連續的投影結果進行時間軸的離散傅立葉轉換，得到步態特徵向量。最後我們使用機率神經網路做為步態辨識的分類器。此外，我們還使用了粒子群最佳化來對機率神經網路分類器的平滑參數進行最佳化，以得到最好的辨識效果。
實驗結果顯示，我們所提出的步態辨識方法具有很高的辨識性能。最後我們將整個步態辨識的演算法移植到具有ARM處理器及DSP處理器的雙核心嵌入式系統上，分析每個演算法模組的計算複雜度，最後將所有模組切割分配在雙核心系統上，以得到最佳的嵌入式系統效能表現。

Because people attach great importance to property and home security, the demand for video surveillance increases gradually. However, traditional video surveillance can only record videos, lacks the ability of instant analysis and decision for videos. Accordingly, people pay more and more attention to the development of intelligent video surveillance system. As a result, we propose a gait-based person identification system which can offer real-time video analysis and recognize person identification.
The procedures of our gait recognition system are gait image segmentation, gait feature extraction and probabilistic neural network classifier(PNN classifier). In gait image segmentation, we build a background model to get the foreground object by image subtraction. In gait feature extraction, we compute the vertical and horizontal projection of gait images, and perform Discrete Fourier Transform (DFT) to the projection result. After DFT, we use the result as our gait feature vector. Finally, we use PNN classifier to perform gait recognition. Additionally, we optimize the smoothing parameters of PNN by particle swarm optimization (PSO) in order to get the best recognized performance.
The experimental results show that our proposed method reaches high recognized performance. We then implement our gait recognition system on the dual-core embedded system OMAP3530, which contains an ARM processor and a digital signal processor. We analyze the complexity of every function in our method, and let the function with higher complexity perform on DSP to reach excellent system performance.

[1] Xiaxi Huang and N. Boulgouris, "Model-based human gait recognition using
fusion of features," Acoustics, Speech and Signal Processing, 2009. ICASSP 2009.
IEEE International Conference on, 2009, pp. 1469-1472.
[2] Sungjun Hong, Heesung Lee, and Euntai Kim, "Fusion of multiple gait cycles for
human identification," ICCAS-SICE, 2009, 2009, pp. 3171-3175.
[3] J. Han and B. Bhanu, "Individual recognition using gait energy image," Pattern
Analysis and Machine Intelligence, IEEE Transactions on, vol. 28, 2006, pp.
316-322.
[4] T. Lam and R. Lee, "A New Representation for Human Gait Recognition: Motion
Silhouettes Image (MSI)," Advances in Biometrics, 2005, pp. 612-618.
[5] L. Rustagi, L. Kumar, and G. Pillai, "Human Gait Recognition Based on Dynamic
and Static Features Using Generalized Regression Neural Network," Machine
Vision, 2009. ICMV ''09. Second International Conference on, 2009, pp. 64-68.
[6] K. Moustakas, D. Tzovaras, and G. Stavropoulos, "Gait Recognition Using
Geometric Features and Soft Biometrics," Signal Processing Letters, IEEE, vol.
17, 2010, pp. 367-370.
[7] D. Ioannidis, D. Tzovaras, I. Damousis, S. Argyropoulos, and K. Moustakas, "Gait
Recognition Using Compact Feature Extraction Transforms and Depth
Information," Information Forensics and Security, IEEE Transactions on, vol. 2,
2007, pp. 623-630.
[8] Ju Han and B. Bhanu, "Statistical feature fusion for gait-based human
recognition," Computer Vision and Pattern Recognition, 2004. CVPR 2004.
Proceedings of the 2004 IEEE Computer Society Conference on, 2004, pp.
II-842-II-847 Vol.2.
[9] Dong Ming, Zhaojun Xue, Lin Meng, Baikun Wan, Yong Hu, and K. Luk,
"Identification of humans using infrared gait recognition," Virtual Environments,
Human-Computer Interfaces and Measurements Systems, 2009. VECIMS ''09.
IEEE International Conference on, 2009, pp. 319-322.
[10] D. Gafurov, J. Hagen, and E. Snekkenes, "Temporal Characteristics of Gait
Biometrics," Computer Engineering and Applications (ICCEA), 2010 Second
International Conference on, 2010, pp. 557-561.
[11] C. Bauckhage, J. Tsotsos, and F. Bunn, "Detecting abnormal gait," Computer
and Robot Vision, 2005. Proceedings. The 2nd Canadian Conference on , vol., no.,
pp. 282- 288, 9-11 May 2005
[12] Yunfeng Wu and S. Krishnan, "Statistical Analysis of Gait Rhythm in Patients
With Parkinson''s Disease," Neural Systems and Rehabilitation Engineering, IEEE
Transactions on, vol. 18, 2010, pp. 150-158.
[13] W. Long and Y.H. Yang, "Stationary background generation: an alternative to the
difference of two images," in Proc. Of Pattern Recognition, Vol. 23, No. 12,
pp.1351-1359, 1990.
[14] A. H. S. Lai and N.H.C. Yung , "A fast and accurate scoreboard algorithm for
estimating stationary backgrounds in an image sequences," in Proc. Of IEEE Int’s
Symp. On Circuits and Systems, Vol. 4, pp.241-244, 1998.
[15] S.Y. Chen, S. Y. Ma, and L. G. Chen, "Efficient moving object segmentation
algorithm using background registration technique," IEEE Transactions on
Circuits and Systems for Video Technology , pp. 577-586,2002.
[16] D. W. Lim, S.H. Choi, and J.S. Jun, "Automated detection of all kinds of
violations at a street intersection using real Time individual vehicle tracking," in
Proc. Of Fifth IEEE Southwest Symposium on Image Analysis and Interpretation,
pp.126-129, 2002.
[17] Y. K. Jung and Y.S. Ho, "Traffic parameter extraction using video-based vehicle
tracking," in Proc. Of IEEE Int’l Conf. on Intelligent Transportation Systems,
pp.764-769, 1999.
[18] Y. C. Chung, J. M. Wang, and S. W. Chen, "Progressive Background Image
Generation." in Proc. of 15th IPPR Conf. on Computer Vision, Graphics and
Image Processing, pp. 858-865, 2002.
[19] J. Dowdall, I. Pavlidis, and G. Bebis, “Face Detection in the Near-IR Spectrum,”
Image and Vision Computing, vol. 21, Jul. 2003, pp. 565-578.
[20] Ling Lu, Yong Yang, Lei Wang, and Bin Tang, “Eye Location Based on Gray
Projection,” Intelligent Information Technology Application, 2009. IITA 2009.
Third International Symposium on, 2009, pp. 58-60.
[21] Xiang-tao Chen, Zhi-hui Fan, Hui Wang, and Zhe-qing Li, "Automatic Gait
Recognition Using Kernel Principal Component Analysis," Biomedical
Engineering and Computer Science (ICBECS), 2010 International Conference on,
2010, pp. 1-4.
[22] Su-li Xu and Qian-jin Zhang, "Gait Recognition Using Fuzzy Principal
Component Analysis," e-Business and Information System Security (EBISS),
2010 2nd International Conference on, 2010, pp. 1-4.
[23] Dong Ming, Yanru Bai, Cong Zhang, Baikun Wan, Yong Hu, and K. Luk, "Novel
gait recognition technique based on SVM fusion of PCA-processed contour
projection and skeleton model features," Computational Intelligence for
Measurement Systems and Applications, 2009. CIMSA ''09. IEEE International
Conference on, 2009, pp. 1-4.
[24] Bashir, K., Tao Xiang, and Shaogang Gong, "Feature selection on Gait Energy
Image for human identification," Acoustics, Speech and Signal Processing, 2008.
ICASSP 2008. IEEE International Conference on , vol., no., pp.985-988, March
31 2008-April 4 2008
[25] Guo-Chang Huang and Yun-Hong Wang, "Human gait recognition based on X-T
plane energy images," Wavelet Analysis and Pattern Recognition, 2007. ICWAPR
''07. International Conference on, 2007, pp. 1134-1138.
[26] Shiqi Yu, Daoliang Tan, and Tieniu Tan, "A Framework for Evaluating the Effect
of View Angle, Clothing and Carrying Condition on Gait Recognition," Pattern
Recognition, 2006. ICPR 2006. 18th International Conference on, 2006, pp.
441-444.
[27] Ju Han and B. Bhanu, "Statistical feature fusion for gait-based human
recognition," Computer Vision and Pattern Recognition, 2004. CVPR 2004.
Proceedings of the 2004 IEEE Computer Society Conference on, 2004, pp.
II-842-II-847 Vol.2.
[28] S. S. Ghidary, Y. Nakata, T. Takamori, and M. Hattori," Human Detection and
Localization at Indoor Environment by Home Robot," in Proc. of IEEE
International Conference of Systems, Man, and Cybernetics, Volume 2, pp.1360 -
1365, Oct. 2000.
[29] K. K. Ken, S. H. Cho, H. J. Kim, and J. Y. Lee, "Detecting and tracking moving
object using an active camera." in Proc. of 7th International Conference of
Advanced Communication Technology, ICACT , Volume 2, pp. 817 - 820 ,2005.
[30] L. Maddalena and A. Petrosino, "A Self-Organizing Approach to Background
Subtraction for Visual Surveillance Applications," IEEE Transactions on Image
Processing, Volume 17, pp. 1168 - 1177 ,2008.
[31] [Online.] CASIA website, "CASIA Gait Database", http://www.cbsr.ia.ac.cn/engl
lish /Gait Databases.asp
[32] J.W. Cooley, and J.W. Tukey, "An Algorithm for the Machine Computation of the
Complex Fourier Series," Mathematics of Computation, Vol. 19, pp. 297-301,
1965.
[33] D.F. Specht, "Probabilistic neural networks," Neural Networks, vol. 3, 1990, pp.
109-118.
[34] J. Kennedy and R. Eberhart, "Particle swarm optimization, " in Proc. Of IEEE
International Conference on Neural Networks, Volume 4, pp.1942 - 1948, 27
Nov.-1 Dec. 1995.
[35] [Online.] Texas Instruments website, "OMAP3530/25 Applications Processor
Datasheet", http://www.ti.com/lit/gpn/omap3530
[36] [Online.] REALTIMEDSP website, "ICETEK-OMAP3530-MINI", http://www.r
ealti medsp.com.cn/product/detail.asp?ID=291
[37] Qian Tao and R. Veldhuis, "Biometric Authentication System on Mobile
Personal Devices, " Instrumentation and Measurement, IEEE Transactions on, vol.
59, 2010, pp. 763-773.
[38] A. Bazin and M. Nixon, "Gait Verification Using Probabilistic Methods, "
Application of Computer Vision, 2005. WACV/MOTIONS ''05 Volume 1. Seventh
IEEE Workshops on, 2005, pp. 60-65.
[39] M.C. Yen, 2009, "An Embedded System for Real-Time Detection and Tracking
of Moving Object", Master Thesis, National Central University, Department of
Computer Science and Information Engineering, page 8-22.
[40] [Online.] BioID web site, "About FAR, FRR and EER", http://support.bioid.com/
sdk/docs/About_EER.htm