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Improved Gait Classification with Different Smoothing Techniques

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@article{IJASEIT51,
   author = {Hu Ng and Hau-Lee Tong and Wooi Haw Tan and Junaidi Abdullah},
   title = {Improved Gait Classification with Different  Smoothing Techniques},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {1},
   number = {3},
   year = {2011},
   pages = {242--247},
   keywords = {Biometric; Gait analysis; Fuzzy k-nearest neighbour; Outlier filter},
   abstract = {Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime by identifying the criminal or suspect. This paper present a method to improve gait classification results by applying smoothing techniques on the extracted gait features. The proposed approach is consisted of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by fuzzy k-nearest neighbours (KNN). The extracted gait features are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the recognition rate, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on a dataset of nine subjects walking bidirectionally on an indoor pathway with twelve different covariate factors. From the experimental results, it can be concluded that the proposed approach is effective in gait classification.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=51},
   doi = {10.18517/ijaseit.1.3.51}
}

EndNote

%A Ng, Hu
%A Tong, Hau-Lee
%A Tan, Wooi Haw
%A Abdullah, Junaidi
%D 2011
%T Improved Gait Classification with Different  Smoothing Techniques
%B 2011
%9 Biometric; Gait analysis; Fuzzy k-nearest neighbour; Outlier filter
%! Improved Gait Classification with Different  Smoothing Techniques
%K Biometric; Gait analysis; Fuzzy k-nearest neighbour; Outlier filter
%X Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime by identifying the criminal or suspect. This paper present a method to improve gait classification results by applying smoothing techniques on the extracted gait features. The proposed approach is consisted of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by fuzzy k-nearest neighbours (KNN). The extracted gait features are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the recognition rate, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on a dataset of nine subjects walking bidirectionally on an indoor pathway with twelve different covariate factors. From the experimental results, it can be concluded that the proposed approach is effective in gait classification.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=51
%R doi:10.18517/ijaseit.1.3.51
%J International Journal on Advanced Science, Engineering and Information Technology
%V 1
%N 3
%@ 2088-5334

IEEE

Hu Ng,Hau-Lee Tong,Wooi Haw Tan and Junaidi Abdullah,"Improved Gait Classification with Different  Smoothing Techniques," International Journal on Advanced Science, Engineering and Information Technology, vol. 1, no. 3, pp. 242-247, 2011. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.1.3.51.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ng, Hu
AU  - Tong, Hau-Lee
AU  - Tan, Wooi Haw
AU  - Abdullah, Junaidi
PY  - 2011
TI  - Improved Gait Classification with Different  Smoothing Techniques
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 1 (2011) No. 3
Y2  - 2011
SP  - 242
EP  - 247
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Biometric; Gait analysis; Fuzzy k-nearest neighbour; Outlier filter
N2  - Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime by identifying the criminal or suspect. This paper present a method to improve gait classification results by applying smoothing techniques on the extracted gait features. The proposed approach is consisted of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by fuzzy k-nearest neighbours (KNN). The extracted gait features are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the recognition rate, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on a dataset of nine subjects walking bidirectionally on an indoor pathway with twelve different covariate factors. From the experimental results, it can be concluded that the proposed approach is effective in gait classification.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=51
DO  - 10.18517/ijaseit.1.3.51

RefWorks

RT Journal Article
ID 51
A1 Ng, Hu
A1 Tong, Hau-Lee
A1 Tan, Wooi Haw
A1 Abdullah, Junaidi
T1 Improved Gait Classification with Different  Smoothing Techniques
JF International Journal on Advanced Science, Engineering and Information Technology
VO 1
IS 3
YR 2011
SP 242
OP 247
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Biometric; Gait analysis; Fuzzy k-nearest neighbour; Outlier filter
AB Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime by identifying the criminal or suspect. This paper present a method to improve gait classification results by applying smoothing techniques on the extracted gait features. The proposed approach is consisted of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by fuzzy k-nearest neighbours (KNN). The extracted gait features are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the recognition rate, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on a dataset of nine subjects walking bidirectionally on an indoor pathway with twelve different covariate factors. From the experimental results, it can be concluded that the proposed approach is effective in gait classification.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=51
DO  - 10.18517/ijaseit.1.3.51