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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제18권 제5호
- 발행연도
- 2016.1
- 수록면
- 2,337 - 2,344 (8page)
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초록· 키워드
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In this paper, we proposed a hybrid system of two powerful machine learning schemes, namely the generative model HMM (hidden Markov model) and the discriminative model SVM (support vector machines), which can concertedly enhance the accuracy of human action recognition. The basic idea of our hybrid method is that the temporal characteristics of the sequence data are modeled by HMM state transitions and both the SVM model and Platt’s method obtained through the preprocessing are used to compute the emission probabilities of observations given at each state in HMM. In order to evaluate the performance of the proposed model, we have conducted an experiment that is able to compare our hybrid model with other classical methods using a KTH dataset. From our experimental results, we note that our hybrid approach shows better or poor performance than existing methods in various fields of human action.
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참고문헌 (14)
참고문헌 신청
Cheng, G. / 2015 / Advances in human action recognition: a survey / Proceeding of Computer Vision and Pattern Recognition :1~30
Han, S. / 2015 / SVM-HMM based human behavior recognition / Lecture Notes in Computer Science 8944:93~103
Holte, M. B. / 2012 / Human pose estimation and activity recognition from multi-view videos : comparative explorations of recent developments / IEEE Journal of Selected Topics in Signal Processing 6(5):538~552
김대학 / 2004 / Bootstrap Prediction Interval estimator of SVM / Journal of The Korean Data Analysis Society 6(3):655~662
김대학 / 2005 / On-Line LS-SVM Regression with Pruned Support Vectors - based on cross validatory choice of hyper-parameters - / Journal of The Korean Data Analysis Society 7(5):1539~1546
Han, S. / 2015 / SVM-HMM based human behavior recognition / Lecture Notes in Computer Science 8944:93~103
Holte, M. B. / 2012 / Human pose estimation and activity recognition from multi-view videos : comparative explorations of recent developments / IEEE Journal of Selected Topics in Signal Processing 6(5):538~552
김대학 / 2004 / Bootstrap Prediction Interval estimator of SVM / Journal of The Korean Data Analysis Society 6(3):655~662
김대학 / 2005 / On-Line LS-SVM Regression with Pruned Support Vectors - based on cross validatory choice of hyper-parameters - / Journal of The Korean Data Analysis Society 7(5):1539~1546
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