Probabilistic gait modelling and recognition
Biometric researchers have recently found considerable applicability of gait recognition in visual surveillance systems. This study proposes a probabilistic framework for gait modelling that is applied to gait recognition. The basic idea of this framework is to consider the silhouette shape as a mul...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-02-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2011.0234 |
| _version_ | 1797685950162141184 |
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| author | Sungjun Hong Heesung Lee Euntai Kim |
| author_facet | Sungjun Hong Heesung Lee Euntai Kim |
| author_sort | Sungjun Hong |
| collection | DOAJ |
| description | Biometric researchers have recently found considerable applicability of gait recognition in visual surveillance systems. This study proposes a probabilistic framework for gait modelling that is applied to gait recognition. The basic idea of this framework is to consider the silhouette shape as a multivariate random variable and model it in a full probabilistic framework. The Bernoulli mixture model is employed to model silhouette distribution and recursive algorithms are provided for silhouette image and sequence classification. Finally, the proposed probabilistic method is applied to benchmark databases and its validity is demonstrated through experiments. |
| first_indexed | 2024-03-12T00:58:38Z |
| format | Article |
| id | doaj.art-f16a19221f5c4883bc4cc7773c933e73 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:58:38Z |
| publishDate | 2013-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-f16a19221f5c4883bc4cc7773c933e732023-09-14T10:25:48ZengWileyIET Computer Vision1751-96321751-96402013-02-0171567010.1049/iet-cvi.2011.0234Probabilistic gait modelling and recognitionSungjun Hong0Heesung Lee1Euntai Kim2School of Electrical and Electronic EngineeringYonsei UniversityC613, 134 Sinchon‐dongSeodaemun‐guSeoul120‐749KoreaSchool of Electrical and Electronic EngineeringYonsei UniversityC613, 134 Sinchon‐dongSeodaemun‐guSeoul120‐749KoreaSchool of Electrical and Electronic EngineeringYonsei UniversityC613, 134 Sinchon‐dongSeodaemun‐guSeoul120‐749KoreaBiometric researchers have recently found considerable applicability of gait recognition in visual surveillance systems. This study proposes a probabilistic framework for gait modelling that is applied to gait recognition. The basic idea of this framework is to consider the silhouette shape as a multivariate random variable and model it in a full probabilistic framework. The Bernoulli mixture model is employed to model silhouette distribution and recursive algorithms are provided for silhouette image and sequence classification. Finally, the proposed probabilistic method is applied to benchmark databases and its validity is demonstrated through experiments.https://doi.org/10.1049/iet-cvi.2011.0234probabilistic gait modellinggait recognitionvisual surveillance systemsmultivariate random variableBernoulli mixture modelsilhouette distribution model |
| spellingShingle | Sungjun Hong Heesung Lee Euntai Kim Probabilistic gait modelling and recognition IET Computer Vision probabilistic gait modelling gait recognition visual surveillance systems multivariate random variable Bernoulli mixture model silhouette distribution model |
| title | Probabilistic gait modelling and recognition |
| title_full | Probabilistic gait modelling and recognition |
| title_fullStr | Probabilistic gait modelling and recognition |
| title_full_unstemmed | Probabilistic gait modelling and recognition |
| title_short | Probabilistic gait modelling and recognition |
| title_sort | probabilistic gait modelling and recognition |
| topic | probabilistic gait modelling gait recognition visual surveillance systems multivariate random variable Bernoulli mixture model silhouette distribution model |
| url | https://doi.org/10.1049/iet-cvi.2011.0234 |
| work_keys_str_mv | AT sungjunhong probabilisticgaitmodellingandrecognition AT heesunglee probabilisticgaitmodellingandrecognition AT euntaikim probabilisticgaitmodellingandrecognition |