| 研究生: |
羅偉瑜 Wei-Yu Lo |
|---|---|
| 論文名稱: |
結合人物追蹤與步態辨識的智慧型視訊監控系統 A Smart Surveillance System with user-tracking and Human Gait Recognition |
| 指導教授: |
陳慶瀚
Ching-Han Chen |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
資訊電機學院 - 資訊工程學系在職專班 Executive Master of Computer Science & Information Engineering |
| 論文出版年: | 2018 |
| 畢業學年度: | 106 |
| 語文別: | 中文 |
| 論文頁數: | 56 |
| 中文關鍵詞: | 步態辨識 、定位 |
| 外文關鍵詞: | gait, homography matrix |
| 相關次數: | 點閱:11 下載:0 |
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目前智慧型監控系統多只具備追蹤或是辨識單一功能,且追蹤功能容易受限於收發訊號距離和障礙物,本論文所提出的一個新的智慧型監控系統同時具備追蹤定位和身分辨識的功能。追蹤定位系統架構由兩個部分組成,首先是追蹤定位的仰賴於視訊定位方法,利用投影方法得知影像座標和真實環境座標之間的關係,並且利用此關係求出目前人物的座標位置。辨識方面利用步態辨識方法去辨識出行人的身分。無論是追蹤定位或是步態辨識功能都不需使用者配戴隨身裝置,環境部分也無須架設大量的監控錄影機,大幅節省架設成本。從資料庫驗證和實錄影片進行測試時得到,定位準確度可達到95%以上,辨識正確率也可達到87%。兩樣功能結合起來的貢獻度,不只具備單一功能的特點,還多了雙重確認、節省成本、降低環境限制的其他優點。
Most current smart monitoring systems feature only one function (i.e., tracking or identification function). In addition, the identification function is prone to the interference of obstacles and the distance between the signal sender and recipient. In this study, a novel smart monitoring system featuring both tracking–positioning and identification functions was developed. The proposed system involved two components. First, a video positioning method was adopted for tracking the device location. This method entailed employing a projection method to convert image coordinates to real-world coordinates, and accordingly, to determine the coordinates of the person in question. The identity recognition function of the proposed system used the gait cycle of a person to reveal his or her identity. Therefore, neither the tracking–positioning nor gait recognition function required the person in question to wear portable devices or entailed installing a large number surveillance cameras in the environment; this substantially lowered installation costs. Tests performed using a database and recorded videos verified that the smart monitoring system introduced in this study demonstrated a position accuracy and identification accuracy of 95% and 87%, respectively. Overall, the proposed system offers two monitoring functions, enables double confirmation, reduces costs, and overcomes environmental restrictions.
[1] Vasanth Subramanian, Sunil Dev Choudhary B, Lalithamani N,” A Survey of ExtantSurveillance Systems using Biometric Tracking”, IEEE Computing and Communication Systems , Pages: 1 – 6,2017, DOI: 10.1109/ICACCS.2017.8014664
[2] Lee, L., Grimson, W.: ‘Gait analysis for recognition and classification’ Fifth
IEEE Int.Conf. on Automatic Face and Gesture Recognition, May 2002, pp.148–155
[3] Jie Xu,Lingjie Duan,Rui Zhang,"Surveillance and Intervention of Infrastructure-Free
Mobile Communications: A New Wireless Security Paradigm",IEEE Communications
Society, DOI:10.1109/MWC.2017.1600279,page:152 – 159
[4]D. Sunehra and A. Bano, "An intelligent surveillance with cloud storage for home
security," in 2014 Annual IEEE India Conference(INDICON), 2014, pp. 1-6.
[5]Fiaz, M.K.; Ijaz, B. Vision based Human Activity Tracking using Artificial Neural
Networks. In Proceedings of IEEE International Conference on Intelligent and
Advanced Systems (ICIAS), Kuala Lumpur, Malaysia, 15–17 June 2010; pp. 1–5.
[6]Foroughi, H.; Aski, B.S.; Pourreza, H. Intelligent Video Surveillance for Monitoring
Fall Detection of Elderly in Home Environments. In Proceedings of the IEEE 11th
International Conference on Computer and Information Technology (ICCIT), Khulna,
Bangladesh, 24–27 December 2008; pp. 219–224;
[7] Muddsser Hussain, Rong Xie, Liang Zhang, Mehmood Nawaz, Malik Asfandyar
“Multi-Target Tracking Identification System under Multi-Camera Surveillance
System,” Progress in Informatics and Computing (PIC), 2016 International Conference
on, Pages: 311 – 316,2016,DOI: 10.1109/PIC.2016.7949516
[8] Liu, C.; Chung, P.; Chung, Y.; Thonnat, M. Understanding of human behaviors from
videos in nursing care monitoring systems. J. High Speed Netw. 2007, 16, 91–103.
[9] S.W. Lee, K. Mase and K. Kogure, “Detection of Spatio-Temporal Gait Parameters by
Using Wearable Motion Sensors,” Proceedings of IEEE Engineering in Medicine and
Biology Society, pp. 6836-6839, 2005.
[10] Shuang Song, Xiaoxiao Qiu, Jiaole Wang, and Max Q.-H. Meng,“Design and
Optimization Strategy of Sensor Array Layout for Magnetic Localization System”,
2017 IEEE Sensors Council,Volume:17,Issue:6,Pages:1849-1857,
DOI:10.1109/JSEN.2017.2652470
[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] B. DeCann and A. Ross, “Gait Curves for Human Recognition, Backpack Detection
and Silhouette Correction in a Nighttime Environment,” in Proceedings of SPIE
Conference on Biometric Technology for Human Identification VII, Orlando,
USA ,Apr. 2010.
[13] L. Lee and W. Grimson, “Gait Analysis for Recognition and Classification,” in
Proceedings of International Conference on Automatic Face and
GestureRecognition,Washington, DC, pp. 148- 155, May 2002.
[14] Toshiyo Tamura; Isao Mizukura; Masaki Sekine; Yutaka Kimura, “Monitoring and Evaluation of Blood Pressure Changes With a Home Healthcare System”, IEEE Transactions on Information Technology in Biomedicine, 2011,Volume:15,Issue:4, Pages:602-607,DOI:10.1109/TITB.2011.2156804
[15] Muhammad Muaaz; Rene Mayrhofer,“Smartphone-based Gait Recognition: From Authentication to Imitation”,IEEE Transactions on Mobile Computing, 2017, Volume: PP, Issue: 99,Pages: 1 - 1, DOI: 10.1109/TMC.2017.2686855
[16] X. Li, S. Maybank, and D. Tao, “Gender Recognition Based on Local BodyMotions,”in Proceedings of International Conference on Systems, Man, and Cybernetics,Montreal, Quebec, pp. 3881-3886, Oct. 2007.
[17] 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.
[18] 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.
[19] 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.
[20 ]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.
[21] 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.
[22] Sungjun Hong, Heesung Lee, and Euntai Kim, "Fusion of multiple gait cycles for human identification," ICCAS-SICE, 2009, 2009, pp. 3171-3175.
[23] Zifeng Wu; Yongzhen Huang; Liang Wang; Xiaogang Wang; Tieniu Tan,” A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, Volume: 39, Issue: 2,pp: 209 - 226,
[24] Gracian Trivino; Oscar Cordon,"Human Gait Modeling Using a Genetic Fuzzy Finite State Machine",IEEE Transactions on Fuzzy Systems,2012, Volume:20,Issue:2,Pages:205-223, DOI: 10.1109/TFUZZ.2011.2171973
[25] Songmin Jia; Lijia Wang; Xiuzhi Li,"View-invariant gait authentication based on silhouette contours analysis and view estimation,"IEEE/CAA Journal of Automatica Sinica,Year:2015,Volume:2,Issue:2,Pages:226-232
[26] D. Young and J. Ferryman, “PETS metrics: Online performance evaluation service,” In
Proc. IEEE Int. Workshop on Performance Evaluation of Tracking Systems, pp. 317–324, 2005.
[27] N. Dalal and B. Triggs, “Histogram of Oriented Gradients for Human
Detection, ” IEEE Conference on Computer Vision and Pattern Recognition,
San Diego, CA, USA, 2005, pp.886-893.
[28] Gafurov, D.: ‘A survey of biometric gait recognition: approaches, security and
challenges’. IEEE Int. Conf. on Biometrics: Theory, Applications and
Systems, Norwegian, 2007, pp. 1–12
[29] Omar Costilla-Reyes; Patricia Scully; Krikor B. Ozanyan,”Temporal
Pattern Recognition in Gait Activities Recorded With a Footprint Imaging Sensor System”, IEEE Sensors Journal,2016, Volume:16, Issue: 24Pages: 8815 - 8822,
[30] Zhiyong Liu,” Gait recognition using Active Energy Image and Gabor wavelet”, 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI),2016.pp: 1354 - 1358
[31] Wenzheng Chi; Jiaole Wang; Max Q. -H. Meng,” A Gait Recognition Method for Human Following in Service Robots”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, Volume: PP, Issue: 99, Pages: 1 - 12
[32] Mohamed H. Zaki; Tarek Sayed,” Exploring walking gait features for the automated recognition of distracted pedestrians”, IET Intelligent Transport Systems, 2016, Volume:10, Issue: 2
[33] Saad M. Darwish,” Design of adaptive biometric gait recognition algorithm with freewalking directions”,IET Biometrics,2017,Volume:6,Issue:2,pp: 53 - 60,
[34] Ye Hanmin; Huang Peiliang” Gait recognition based on feature fusion and support vector machine”,2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), 2016,pp: 281 – 284
[35] Michal Balazia; Konstantinos N. Plataniotis,” Human gait recognition from motion capture data in signature poses”,IET Biometrics, 2017, Volume: 6, Issue: 2,Pages: 129 - 137
[36] Jin Tang; Jian Luo; Tardi Tjahjadi; Fan Guo,” Robust Arbitrary-View Gait
Recognition Based on 3D Partial Similarity Matching”, IEEE Transactions on Image Processing,2017, Volume: 26, Issue: 1, pp: 7 - 22
[37] Tanmay Verlekar; Paulo Correia; Luis Soares,” View-Invariant Gait Recognition Exploiting Spatio-Temporal Information and a Dissimilarity Metric”, 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), 2016,pp: 1 - 6,
[38] Zifeng Wu; Yongzhen Huang; Liang Wang; Xiaogang Wang; Tieniu Tan,” A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, Volume: 39, Issue: 2,pp: 209 - 226,
[39] Vasanth Subramanian, Sunil Dev Choudhary B, Lalithamani N,”A Survey of Extant Surveillance Systems using Biometric Tracking”, IEEE Computing and Communication Systems , Pages: 1 – 6,2017, DOI: 10.1109/ICACCS.2017.8014664
[40] S.W. Lee, K. Mase and K. Kogure, “Detection of Spatio-Temporal Gait Parameters by
Using Wearable Motion Sensors,” Proceedings of IEEE Engineering in Medicine and
Biology Society, pp. 6836-6839, 2005.
[41] Jen-Kai Hsueh, Yi-Ching Huang, Bing-Ting Dong, and Ching-Min Lee,“An
Embedded Cloud Sur veillance System Design”, IEEE International Conference on
Consumer Electronics, DOI: 10.1109/ICCE-China.2017.7991034
[42] 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.