Abstract
Human behaviour analysis helps to monitor a person’s daily activities and detect home care emergencies. Classifying posture is an important step of human behaviour analysis. Many studies improve the accuracy of classifying. However, the number of features is big or extracting these features uses complicated formulas. Therefore, we proposed two features with simple computing. Two new features are formulated from the height and the square showing the human body’s silhouettes. Then, we choose a non-linear Support Vector Machine to classify postures based on proposed features. Experiments show Support Vector Machine classify effectively and better than other methods.
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References
Juang, C.-F., Chen, T.-C., Du, W.-C.: Human body 3D posture estimation using significant points and two cameras. Sci. World J. 2014, 17 (2014)
Juang, C.-F., Liang, C.-W., Lee, C.-L., Chung, I.-F.: Vision-based human body posture recognition using support vector machines. In: International Conference on Awareness Science and Technology, pp. 150–155 (2012)
Wu, C., Aghajan, H.: Model-based human posture estimation for gesture analysis in an opportunistic fusion smart camera network. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 453–458 (2007)
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)
MacKay, D.: Information Theory, Inference and Learning Algorithms, pp. 284–292. Cambridge University Press, Cambridge (2003). ISBN: 0-521-64298-1
McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, Hoboken (2000)
Cohen, I., Li, H.: Inference of human postures by classification of 3D human body shape. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 74–81 (2003)
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. ACM Commun. 56, 116–124 (2013)
Goldmann, L., Karaman, M., Sikor, T.: Human body posture recognition using MPEG-7 descriptors. In: Visual Communications and Image Processing, pp. 18–22 (2004)
Hoa, N.T., Bui, T.D.: A new effective learning rule of Fuzzy ART. In: Conference on Technologies and Applications of Artificial Intelligence, pp. 224–231. IEEE Press (2012)
Zerrouki, N., Houacine, A.: Automatic classification of human body postures based on the truncated SVD. J. Adv. Comput. Netw. 2, 58–62 (2014)
Cucchiara, R., Grana, C., Prati, A., Vezzani, R.: Probabilistic posture classification for human-behavior analysis. IEEE Trans. Syst. Man Cybern. 35, 42–54 (2005)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)
Girondel, V., Bonnaud, L., Caplier, A., Rombaut, M.: Belief theory-based classifiers comparison for static human body postures recognition in video. World Acad. Sci. Eng. Technol. 4, 627–630 (2007)
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Van Tao, N., Hoa, N.T., Truong, Q.X. (2017). Classifying Human Body Postures by a Support Vector Machine with Two Simple Features. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_21
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DOI: https://doi.org/10.1007/978-3-319-49073-1_21
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