Human Action Recognition Using a Hybrid Method of a Generative Model and a Discriminative Model

• 발행기관: 한국자료분석학회
• 저자명: 조완현(Wanhyun Cho)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.18 No.5, 2337~2344쪽, 전체 8쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2016.10.30

영문 초록

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.

목차

1. Introduction

2. Hybrid Method

3. Experiment Results

4. Conclusion

References

키워드

Human action recognition Hybrid schemes Hidden Markov model Support vector machines KTH dataset