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Motion capture as Data Source for Gait-based Human Identification

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PL
Technika motion capture jako źródło danych dla identyfikacji osób na podstawie chodu
Języki publikacji
PL
Abstrakty
PL
Autorzy prezentują wyniki badań nad identyfikacją osób na podstawie danych chodu uzyskanych za pomocą techniki motion capture. Redukcję wymiarowości przeprowadzono stosując algorytm wieloliniowej analizy składowych głównych (MPCA), który operuje na tensorowej reprezentacji danych. Dla potrzeb identyfikacji osób zastosowano szereg metod klasyfikacji dostępnych w pakiecie WEKA uzyskując największą skuteczność dla perceptronu wielowarstwowego. (Technika motion capture jako źródło danych dla identyfikacji osób na podstawie chodu).
EN
The authors present results of the research aiming at human identification based on tensor representation of the gait motion capture data. High-dimensional tensor samples were reduced by means of the multilinear principal component analysis (MPCA). For the purpose of classification the following methods from the WEKA software were used: k Nearest Neighbors (kNN), Naive Bayes, Multilayer Perceptron, and Radial Basis Function Network. The maximum value of the correct classification rate (CCR) was achieved for the classifier based on the multilayer perceptron.
Rocznik
Strony
201--204
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
Bibliografia
  • [1] Boyd J.E., Little J.J., Biometric Gait Recognition, Lecture Notes in Computer Science, 3161 (2005), 19-42
  • [2] Nixon M.S., Tan T.N., Chellappa R., Human Identification Based on Gait. Springer, (2006)
  • [3] Menache A., Understanding Motion Capture for Computer Animation and Video Games. Morgan Kaufmann, (2000)
  • [4] Świtoński A., Mucha R., Danowski D., Mucha M., Cieślar G., Wojciechowski K., Sieroń A., Human identification based on a kinematical data of a gait, Electrical Review, (2011), n.12b, 169-172
  • [5] Xiao J., Zhuang Y., Wu F., Getting Distinct Movements from Motion Capture Data, Proceedings of the International Conference on Computer Animation and Social Agents, (2006), 33-42
  • [6] Pushpa Rani M., Arumugam G., An Efficient Gait Recognition System for Human Identification Using Modified ICA, International Journal of Computer Science & Information Technology, 2 (2010), n.1, 55-67
  • [7] Wang L., Tan T., Ning H., Hu W., Silhouette Analysis-Based Gait Recognition for Human Identification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (2003), n.12, 1505-1518
  • [8] Sarkar S., Phillips P.J., Liu Z., Vega I.R., Grother P., Bowyer K.W., The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (2005), n.12, 162-177
  • [9] Han J., Bhanu B., Individual Recognition Using Gait Energy Image, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (2006), n.2, 316-322
  • [10] Wang L., Ning H., Hu W., Tan T., Gait Recognition Based on Procrustes Shape Analysis, Proceedings of the 9th International Conference on Image Processing, (2002)
  • [11] Hong S., Lee H., Nizami I.F., An S.-J., Kim E., Human Identification Based on Gait Analysis, Proceedings of the International Conference on Control, Automation and Systems, (2007), 2234-2237
  • [12] Kale A., Cuntoor N., Yegnanarayana B., Rajagopalan A.N., Chellappa R., Gait Analysis for Human Identification, Proceedings of the 4th International Conference on Audio- and Video-Based Biometric Person Authentication, (2003), 706-714
  • [13] Sundaresan A., Roy-Chowdhury A., Chellappa R., A Hidden Markov Model Based Framework for Recognition of Humans from Gait Sequences, Proceedings of the 2003 IEEE International Conference on Image Processing, (2003), II-93-96
  • [14] Liu Z., Sarkar S., Improved Gait Recognition by Gait Dynamics Normalization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (2006), n.6, 863-876
  • [15] Chen Ch., Liang J., Zhao H., Hu H., Tian J., Factorial HMM and Parallel HMM for Gait Recognition, IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications andReviews, 39 (2009), n.1, 114-123
  • [16] Krzeszowski T., Kwolek B., Wojciechowski K., Articulated Body Motion Tracking by Combined Particle Swarm Optimization and Particle Filtering, Lecture Notes in Computer Science, 6374 (2010), 147-154
  • [17] Zhang Z., Troje N.F., View-independent person identification from human gait, Neurocomputing, 69 (2005), 250-256
  • [18] Świtoński A., Polański A., Wojciechowski K., Human Identification Based on Gait Paths, Lecture Notes in Computer Science, 6915 (2011), 531-542
  • [19] Lu H., Plataniotis K.N., Venetsanopoulos A.N., MPCA: Multilinear Principal Component Analysis of Tensor Objects, IEEE Transactions on Neural Networks, 19 (2008), n.1, 18-39
  • [20] Law M.H.C., Jain A.K., Incremental nonlinear dimensionality reduction by manifold learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (2006), n. 3, 377-391
  • [21] Yan S., Xu D., Yang Q., Zhang L., Tang X., Zhang H.-J., Discriminant Analysis with Tensor Representation, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2005)
  • [22] Lu H., Plataniotis K.N., Venetsanopoulos A.N., A Survey of Multilinear Subspace Learning for Tensor Data, Pattern Recognition, 44 (2011), n.7, 1540-1551
  • [23] Lu H., Plataniotis K.N., Venetsanopoulos A.N., Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization, Proceedings of the 25th International Conference on Machine Learning, (2008)
  • [24] Lu H., Plataniotis K.N., Venetsanopoulos A.N., Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning, IEEE Transactions on Neural Networks, 20 (2009), n.11, 1820-1836
  • [25] Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H., The WEKA Data Mining Software: An Update, SIGKDD Explorations, 11 (2009), n.1
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS3-0026-0127
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