ICCVG 2012: Computer Vision and Graphics pp 525-532 | Cite as
Gait Identification Based on MPCA Reduction of a Video Recordings Data
Conference paper
Abstract
The scope of this article is gait identification of individuals on the basis of reduced sequences of video recordings data. The gait sequences are considered to be the 3rd-order tensors and its dimensionality is reduced by Multilinear Principal Component Analysis with different values of variation covered. Reduced gait descriptors are identified by the supervised classifiers: Naive Bayes and Nearest Neighbor. CASIA Gait Database ’dataset A’ is chosen to verify the proposed method. The obtained results are promising. For the Naive Bayes and attributes discretization almost 99% of classification accuracy is achieved, which means only one misclassified gait out of eighty validated.
Keywords
Dynamic Time Warping Single Dataset Gait Recognition Motion Capture Data Gait Sequence
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: MPCA: Multilinear Principal Component Analysis of Tensor Objects. IEEE Transactions on Neural Networks 19(1), 18–39 (2008)CrossRefGoogle Scholar
- 2.Świtoński, A., Polański, A., Wojciechowski, K.: Human Identification Based on Gait Paths. In: Proceedings of the 13th International Conference on Advanced Concepts for Intelligent Vision Systems (2011)Google Scholar
- 3.Świtoński, A., Polański, A., Wojciechowski, K.: Human Identification Based on the Reduced Kinematic Data of the Gait. In: 7th International Symposium on Image and Signal Processing and Analysis (2011)Google Scholar
- 4.Benbakreti, S., Benyettou, M.: Recognition Human by gait using PCA and DTW. In: 3th International Conference on Computer Science and its Applications (2011)Google Scholar
- 5.Kulbacki, M., Segen, J., Bak, A.: Unsupervised learning motion models using dynamic time warping. In: Proc. Symp. Intell. Inf. Syst., pp. 217–226 (2002)Google Scholar
- 6.Iwamoto, K., Sonobe, K., Komatsu, N.: A Gait Recognition Method using HMM. In: Proceedings of the 9th European Conference on Computer Vision, vol. 2, pp. 1936–1941. IEEE (2003)Google Scholar
- 7.Cheng, M.-H., Hoa, M.-F., Huanga, C.-L.: Gait analysis for human identification through manifold learning and HMM. Pattern Recognition 41(8), 2541–2553 (2008)CrossRefMATHGoogle Scholar
- 8.Pushpa R., M., Arumugam, G.: An efficient gait recognition system for human identification using modified ICA. International Journal of Computer Science & Information Technology 2(1) (2010) Google Scholar
- 9.Sminchisescu, C., Jepson, A.: Generative Modeling for Continuous Non-Linearly Embedded Visual Inference. In: ICML (2004)Google Scholar
- 10.Tacoob, Y.: Parameterized Modeling and Recognition of Activities. Computer Vision and Image Understanding 73(2), 232–247 (1999)CrossRefGoogle Scholar
- 11.Liang, W., Tieniu, T., Huazhong, N., Weiming, H.: Silhouette Analysis-Based Gait Recognition for Human Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12) (2003)Google Scholar
- 12.Vasilescu, M., Terzopoulos, D.: Multilinear Independent Components Analysis. In: Proceedings of the IEEE Computer Vision and Pattern Recognition Conference (2005)Google Scholar
- 13.Panagakis, Y., Kotropoulos, C., Arce, G.R.: Non-Negative Multilinear Principal Component Analysis of Auditory Temporal Modulations. IEEE Transactions on Audio, Speech, and Language Processing 18(3), 576–588 (2010)CrossRefGoogle Scholar
- 14.Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization. IEEE Transactions on Neural Networks 20(11), 1820–1836 (2009)CrossRefGoogle Scholar
- 15.Josiński, H., Świtoński, A., Jędrasiak, K., Kostrzewa, D.: Human Identification Based on Gait Motion Capture Data. In: Proceeding of the International MultiConference of Engineers and Computer Scientists, vol. I (2012)Google Scholar
- 16.Witten, I.H., Frank, E.: Data Mining: Practical Machnine Learning Tool and TechniquesGoogle Scholar
- 17.Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuousvalued attributes for classification learning. In: Thirteenth International Joint Conference on Articial Intelligence, pp. 1022–1027 (1993)Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2012