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Automatic Human Body Extraction and Posture Analysis in Consecutive Images
|關鍵字:||human body extraction;人體擷取;moving object segmentation;automatic threshold;posture recognition;posture analysis;移動物體分割;自動門檻值;姿態辨識;姿態分析||出版社:||電機工程學系所||引用:|| W. Hu, T. Tan, L. Wang, and S. Maybank, “A survey on visual surveillance of object motion and behaviors,” IEEE Transactions on System, Man, and Cybernetics, Part C: Applications and Reviews, vol.34, no.3, pp. 334-352, Aug. 2004.  N. Friedman and S. Russell, “Image segmentation in video sequences : a probabilistic approach,” Proceedings of Annual Conference on Uncertainty in Artificial Intelligence,1997.  D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russel, “Toward robust automatic traffic scene analysis in real-time,” Proceedings of IAPR International Conference Pattern Recognition, vol. 1, pp. 126-131, 1994.  M. Kohle, D. Merkl, and J. Kastner, “ Clinical gait analysis by neural network : Issues and experiences,” Proceedings of IEEE Symp. Computer-Based Medical system, pp. 138-143, 1997.  W. E. L. 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Pitas, “A novel method for automatic face segmentation, facial feature extraction and tracking,” Signal Processing: Image Communication, vol. 12, pp. 263-281, Jun. 1998.  M. T. Razali and B. J. Adznan, “Detection and classification of moving object for smart vision sensor,” IEEE Department of Computer and Communication Engineering, vol. 1, pp. 733-737, Apr. 2006.  J. Ohya, A. Utsumi, and J. Yamato, Analyzing Video Sequences of Multiple Humans: Tracking, Posture Estimation, and Behavior Recognition, Chap. 3, Kluwer Academic Publishers, 2002.||摘要:||
In this thesis, two kinds of human body posture analysis methods are proposed. One is continuous human body posture recognition by a recurrent fuzzy neural network, and the other is human posture estimation by silhouette and skin color information. Before posture analysis, it is necessary to segment the human body from background. A moving object segmentation algorithm is proposed to distinguish the human body from background from a sequence of images. This algorithm uses an automatic threshold determination method with Euler numbers for frame and background differences. After segmentation, a series of image processing is used obtain a complete silhouette of human body. The objective of posture recognition is to recognize four types of main body postures, including standing, bending, sitting, and lying. The significant Discrete Fourier Transform (DFT) coefficients of horizontal and vertical histograms together with length-width ratio of the silhouette are used as features. Recognizer is designed by a recurrent neural fuzzy network. In posture estimation, our objective is to locate significant body points. We combined skin color information with the convex points of contour of human body to locate head, hands, and feet. Experiment results show that the proposed approach can recognize the four types of postures and locate the significant points of human body with good performance.
|Appears in Collections:||電機工程學系所|
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