A Comparison of Dynamic Naive Bayesian Classifiers and Hidden Markov Models for Gesture Recognition
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidad Nacional Autónoma de México
2011
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| _version_ | 1876477139416514560 |
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| author | H.H. Avilés-Arriaga |
| author_facet | H.H. Avilés-Arriaga |
| contents | A Comparison of Dynamic Naive Bayesian Classifiers and Hidden Markov Models for Gesture Recognition H.H. Avilés-Arriaga L.E. Sucar-Succar C.E. Mendoza-Durán L.A. Pineda-Cortés Ingeniería motion analysis visual tracking Gesture recognition hidden Markov models In this paper we present a study to assess the performance of dynamic naive Bayesian classifiers (DNBCs) versus standard hidden Markov models (HMMs) for gesture recognition. DNBCs incorporate explicit conditional independence among gesture features given states into HMMs. We show that this factorization offers competitive classification rates and error dispersion, it requires fewer parameters and it improves training time considerably in the presence of several attributes. We propose a set of qualitative and natural set of posture and motion attributes to describe gestures. We show that these posture-motion features increase recognition rates significantly in comparison to motion features. Additionally, an adaptive skin detection approach to cope with multiple users and different lighting conditions is proposed. We performed one of the most extensive experimentation presented in the literature to datethat considers gestures of a single user, multiple people and with variations on distance and rotation using a gesture database with 9441 examples of 9 different classes performed by 15 people. Results show the effectiveness of the overall approach and the reliability of DNBCs in gesture recognition. 2011 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47419311007 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.1 Vol.9 |
| format | Artículo científico |
| id | redalyc_47419311007 |
| institution | Redalyc |
| language | en |
| publishDate | 2011 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | A Comparison of Dynamic Naive Bayesian Classifiers and Hidden Markov Models for Gesture Recognition H.H. Avilés-Arriaga Ingeniería motion analysis visual tracking Gesture recognition hidden Markov models A Comparison of Dynamic Naive Bayesian Classifiers and Hidden Markov Models for Gesture Recognition H.H. Avilés-Arriaga L.E. Sucar-Succar C.E. Mendoza-Durán L.A. Pineda-Cortés Ingeniería motion analysis visual tracking Gesture recognition hidden Markov models In this paper we present a study to assess the performance of dynamic naive Bayesian classifiers (DNBCs) versus standard hidden Markov models (HMMs) for gesture recognition. DNBCs incorporate explicit conditional independence among gesture features given states into HMMs. We show that this factorization offers competitive classification rates and error dispersion, it requires fewer parameters and it improves training time considerably in the presence of several attributes. We propose a set of qualitative and natural set of posture and motion attributes to describe gestures. We show that these posture-motion features increase recognition rates significantly in comparison to motion features. Additionally, an adaptive skin detection approach to cope with multiple users and different lighting conditions is proposed. We performed one of the most extensive experimentation presented in the literature to datethat considers gestures of a single user, multiple people and with variations on distance and rotation using a gesture database with 9441 examples of 9 different classes performed by 15 people. Results show the effectiveness of the overall approach and the reliability of DNBCs in gesture recognition. 2011 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47419311007 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.1 Vol.9 |
| title | A Comparison of Dynamic Naive Bayesian Classifiers and Hidden Markov Models for Gesture Recognition |
| topic | Ingeniería motion analysis visual tracking Gesture recognition hidden Markov models |
| url | https://www.redalyc.org/articulo.oa?id=47419311007 |