Path: Top -> Journal -> Telkomnika -> 2014 -> Vol 12, No 4: December
Large Crowd Count Based on Improved SURF Algorithm
Large Crowd Count Based on Improved SURF Algorithm
Journal from gdlhub / 2016-11-15 02:01:38Oleh : Haining Zhang, Huanbo Gao, Telkomnika
Dibuat : 2014-12-01, dengan 1 file
Keyword : crowd count, SURF, GLCM, perspective-correct, SVR
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/362
This paper uses an analysis of Speeded up Robust Feature (SURF), based on the method of Linear Interpolation for camera distortion calibration, for high-density crowd counting. The eigenvalues are built on the Gray Level Co-occurrence Matrix (GLCM) features and the SURF features. Though the method of linear interpolation, weight values are interpolated to reduce the error, which is caused by camera distortion calibration. The optimized crowds feature vector can be got then. Through the method of support vector regression, the crowds number can be forecast by training model. The experiment result shows that the method of this paper has a higher accuracy than the previous methods.
Deskripsi Alternatif :This paper uses an analysis of Speeded up Robust Feature (SURF), based on the method of Linear Interpolation for camera distortion calibration, for high-density crowd counting. The eigenvalues are built on the Gray Level Co-occurrence Matrix (GLCM) features and the SURF features. Though the method of linear interpolation, weight values are interpolated to reduce the error, which is caused by camera distortion calibration. The optimized crowds feature vector can be got then. Through the method of support vector regression, the crowds number can be forecast by training model. The experiment result shows that the method of this paper has a higher accuracy than the previous methods.
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|---|---|
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| Organisasi | Telkomnika |
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| Alamat | Jln. Jenderal Sudirman |
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| Daerah | Jambi |
| Negara | Indonesia |
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