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題名：一種簡易之動作辨識系統及其於「全肢體反應」英語教學互動系統之應用
書刊名：前瞻科技與管理
作者：蘇木春／林紘毅
作者(外文)：Su, Mu-chun／Lin, Hung-yi
出版日期：2013
卷期：3:2
頁次：頁15-27
主題關鍵詞：數位學習；擴增實境；TPR教學；動態時間校正演算法；隱藏式馬可夫模型；Total physical response；E-learning；Augmented system；TPR learning；Time warping algorithm；Hidden Markov model
原始連結：連回原系統網址new window
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  • 被引用次數被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:2
  • 共同引用共同引用:0
  • 點閱點閱:12
人類的動作蘊含了極多的資訊，不同的動作各自代表了不同的企圖及狀態，因此，可被應用於許多方面。本論文將介紹一個以Kinect為基礎之動作辨識系統，藉由此動作辨識系統來實現「全肢體反應」英語教學互動系統。透過市面上常見之Kinect景深攝影機來抓取人體骨架資訊，之後，傳送到電腦端做辨識處理。本系統採用的特徵共分為兩類：第一類為人體關節的向量，為人體脊柱點至左手、右手、左腳以及右腳的向量，第二類的特徵為各關節向量之間的角度。擷取了有效特徵後，就採用動態時間校正演算法來動作分析及辨識。此系統被用來辨識二十二種不同的動作。動態時間校正演算法可達到97.3%的正確辨識率。此篇論文所提出之特徵可有效解決人體高矮胖瘦不同及朝向角度不同的問題。
Human actions imply much information. Different actions represent different attempts and states; therefore, human actions recognition may be applied to many aspects. This paper introduces a Kinect-based action recognition system. Based on the action recognition system, an interactive TPR-based English learning system is implemented. Via a Kinect depth sensor, human skeleton information can be captured and transmitted to a PC for further analysis and recognition. In this system, we propose two kinds of features: the joint vectors and the vectors formed by the connected lines between the human spine point and the two hands and feet. Then we adopt the dynamical time warping algorithm for action analysis and recognition. The performance of the proposed system was evaluated on the recognition of 22 different actions. The simulation results could achieve 97.3% correct recognition rate.
期刊論文
1.Kanhere, N.K.、Birchfield, S.T.(2008)。Real-Time Incremental Segmentation and Tracking of Vehicles at Low Camera Angles Using Stable Features。IEEE Transactions on Intelligent Transportation Systems Processing，9，148-160。  new window
2.Lambert E.G.、Hogan N.L.、Nerbonne, T.、Barton S.M.、Watson, P.L.、Buss, J.、Lambert, J.(2007)。Differences in Forensic Science Views and Needs of Law Enforcement: A Survey of Michigan law Enforcement Agencies。Police Practice and Research，8(5)，415-430。  new window
3.Morel, J.M.、Yu, G.(2009)。ASIFT: A New Framework for Fully Affine Invariant Image Comparison。Siam Journal on Imaging Sciences，2(2)，438-469。  new window
4.Neufeld, P.、Scheck, B.(2010)。Making Forensic Science More Scientific。Nature，464，351。  new window
5.Saks, M. J.、Koehler, J. J.(2005)。The Coming Paradigm Shift in Forensic Identification Science。Science，309(5736)，892-895。  new window
6.Su, C.W.、Liao, H.Y.M.、Tyan, H.R.、Lin, C.W.、Chen, D.Y.、Fan, K.C.(2007)。Motion- Flow Based Video Retrieval。IEEE Transactions on Multimedia，9，1193- 1201。  new window
7.Sun, Z.、Bebis, G.、Miller, R.(2005)。On-Road Vehicle Detection Using Evolutionary Gabor Filter Optimization。IEEE Transactions on Intelligent Transportation Systems，6(2)，125-137。  new window
8.Sun, Z.、Bebis, G.、Miller, R.(2006)。On-Road Vehicle Detection: A Review。IEEE Transactions on Pattern Analysis and Machine Intelligence，28，694-711。  new window
9.Viola, P.、Jones, M.J.(2004)。Robust Real-Time Face Detection。International Journal of Computer Vision，57，137-154。  new window
10.Feldman, J.、Singh, M.(2005)。Information Along Contours and Object Boundaries。Psychological Review，112，243-252。  new window
11.Canny, J.(1986)。A Computational Approach to Edge Detection。IEEE Transactions Pattern Analysis and Machine Intelligence，8(6)，679-698。  new window
12.Lowe, David G.(2004)。Distinctive Image Features from Scale-Invariant Keypoints。International Journal of Computer Vision，60(2)，91-110。  new window
會議論文
1.Bijhold, J.、Ruifrok, A.、Jessen, M.、Geradts, Z.、Ehrhardt, S.、Alberink, I.(2007)。Forensic Audio and Visual Evidence 2004-2007: A Review。Interpol Forensic Science Symposium。Lyon, France。  new window
2.Calderara, S.、Prati, A.、Cucchiara, R.(2009)。Video Surveillance and Multimedia Forensics: An Application to Trajectory Analysis。The First ACM workshop on Multimedia in Forensics。Beijing, China。  new window
3.Caraffi, C.、Vojir, T.、Trefny, J.、Sochman, J.、Matas J.(2012)。A System for Real- Time Detection and Tracking of Vehicles from a Single Car-Mounted Camera。Anchorage, AK, US：IEEE。975-982。  new window
4.Dalal, N.、Triggs, B.(2005)。Histograms of Oriented Gradients for Human Detection。San Diego, CA, US：IEEE。886-893。  new window
5.Ferrari, V.、Tuytelaars, T.、Van Gool L.(2006)。Object Detection by Contour Segment Ne tworks。Berlin, Germany：Springer- Verlag。3，14-28。  new window
6.Franke, K.、Srihari, S.N.(2007)。Computational Forensics: Towards Hybrid-Intelligent Crime Investigation。Third International Symposium on Information Assurance and Security。Manchester, England。  new window
7.Franke, K.、Srihari, S.N.(2008)。Computational forensics: An Overview。Berlin, Germany：Springer- Verlag。1-10。  new window
8.He, X.C.、Yung, N.H.C.(2004)。Curvature Scale Space Corner Detector with Adapt ive Thr e shold and Dynami c Region of Support。Washington, DC, US：IEEE Computer Society Press。2，791-794。  new window
9.Worring, M.、Cucchiara, R.(2009)。Multimedia in Forensics。New York, US：ACM。1153-1154。  new window
10.Lowe, D. G.(1999)。Object Recognition From Local Scale-Invariant Features。The International Conference on Computer Vision。IEEE Computer Society Press。1150-1157。  new window
研究報告
1.Saif Mohammed SA、Al-Kuwari(2011)。Forensic Tracking and Surveillance: Algorithms for Homogeneous and Heterogeneous Settings。UK：University of London。  new window
 
 
 
 
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