Development of Vision Based Multiview Gait Recognition System with MMUGait Database
This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based ga...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/376569 |
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| author | Hu Ng Wooi-Haw Tan Junaidi Abdullah Hau-Lee Tong |
| author_facet | Hu Ng Wooi-Haw Tan Junaidi Abdullah Hau-Lee Tong |
| author_sort | Hu Ng |
| collection | DOAJ |
| description | This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases. |
| format | Article |
| id | doaj-art-603d6c8849f7474f84d45c6bcc81d5a7 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-603d6c8849f7474f84d45c6bcc81d5a72025-02-03T07:25:20ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/376569376569Development of Vision Based Multiview Gait Recognition System with MMUGait DatabaseHu Ng0Wooi-Haw Tan1Junaidi Abdullah2Hau-Lee Tong3Faculty of Computing and Informatics, Multimedia University, 63100 Cyberjaya, MalaysiaFaculty of Computing and Informatics, Multimedia University, 63100 Cyberjaya, MalaysiaFaculty of Computing and Informatics, Multimedia University, 63100 Cyberjaya, MalaysiaFaculty of Computing and Informatics, Multimedia University, 63100 Cyberjaya, MalaysiaThis paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.http://dx.doi.org/10.1155/2014/376569 |
| spellingShingle | Hu Ng Wooi-Haw Tan Junaidi Abdullah Hau-Lee Tong Development of Vision Based Multiview Gait Recognition System with MMUGait Database The Scientific World Journal |
| title | Development of Vision Based Multiview Gait Recognition System with MMUGait Database |
| title_full | Development of Vision Based Multiview Gait Recognition System with MMUGait Database |
| title_fullStr | Development of Vision Based Multiview Gait Recognition System with MMUGait Database |
| title_full_unstemmed | Development of Vision Based Multiview Gait Recognition System with MMUGait Database |
| title_short | Development of Vision Based Multiview Gait Recognition System with MMUGait Database |
| title_sort | development of vision based multiview gait recognition system with mmugait database |
| url | http://dx.doi.org/10.1155/2014/376569 |
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