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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  4/2016 - 16
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 HIGH-IMPACT PAPER 

An Efficient Method of HOG Feature Extraction Using Selective Histogram Bin and PCA Feature Reduction

LAI, C. Q. See more information about LAI, C. Q. on SCOPUS See more information about LAI, C. Q. on IEEExplore See more information about LAI, C. Q. on Web of Science, TEOH, S. S. See more information about TEOH, S. S. on SCOPUS See more information about TEOH, S. S. on SCOPUS See more information about TEOH, S. S. on Web of Science
 
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Download PDF pdficon (1,987 KB) | Citation | Downloads: 955 | Views: 2,837

Author keywords
feature extraction, image analysis, object detection, pattern recognition, computer vision

References keywords
detection(18), vision(9), pattern(9), human(8), pedestrian(7), recognition(6), feature(6), cvpr(6), oriented(5), histogram(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-11-30
Volume 16, Issue 4, Year 2016, On page(s): 101 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.04016
Web of Science Accession Number: 000390675900016
SCOPUS ID: 85007569629

Abstract
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Histogram of Oriented Gradient (HOG) is a popular image feature for human detection. It presents high detection accuracy and therefore has been widely used in vision-based surveillance and pedestrian detection systems. However, the main drawback of this feature is that it has a large feature size. The extraction algorithm is also computationally intensive and requires long processing time. In this paper, a time-efficient HOG-based feature extraction method is proposed. The method uses selective number of histogram bins to perform feature extraction on different regions in the image. Higher number of histogram bin which can capture more detailed information is performed on the regions of the image which may belong to part of a human figure, while lower number of histogram bin is used on the rest of the image. To further reduce the feature size, Principal Component Analysis (PCA) is used to rank the features and remove some unimportant features. The performance of the proposed method was evaluated using INRIA human dataset on a linear Support Vector Machine (SVM) classifier. The results showed the processing speed of the proposed method is 2.6 times faster than the original HOG and 7 times faster than the LBP method while providing comparable detection performance.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 1842] [SCOPUS Times Cited 2347]


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[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 8]


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[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 8]


[4] R. M. Mueid, C. Ahmed, and M. A. R. Ahad, "Pedestrian activity classification using patterns of motion and histogram of oriented gradient," Journal on Multimodal User Interfaces, May 2015.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 11]


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[CrossRef] [Web of Science Times Cited 76] [SCOPUS Times Cited 94]


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[CrossRef] [SCOPUS Times Cited 10]


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[CrossRef] [SCOPUS Times Cited 28]


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[CrossRef] [SCOPUS Times Cited 16]


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[CrossRef] [Web of Science Times Cited 132] [SCOPUS Times Cited 167]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 377] [SCOPUS Times Cited 620]


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[CrossRef] [Web of Science Times Cited 7884]


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[CrossRef] [SCOPUS Times Cited 21]


[17] X. Wang, T. X. Han, and S. Yan, "An HOG-LBP human detector with partial occlusion handling," in Proc. IEEE 12th International Conference on Computer Vision, 2009, pp. 32-39.
[CrossRef] [Web of Science Times Cited 964] [SCOPUS Times Cited 1415]


[18] C. Conde, D. Moctezuma, I. Martín De Diego, and E. Cabello, "HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments," Neurocomputing, vol. 100, pp. 19-30, 1/16/ 2013.
[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 48]


[19] G.-S. Hong, B.-G. Kim, Y.-S. Hwang, and K.-K. Kwon, "Fast multi-feature pedestrian detection algorithm based on histogram of oriented gradient using discrete wavelet transform," Multimedia Tools and Applications, pp. 1-17, 2015.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 15]


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[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 29]


[21] P. Y. Chen, C. C. Huang, C. Y. Lien, and Y. H. Tsai, "An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 2, pp. 656-662, Apr. 2014.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 67]


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[CrossRef]


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[CrossRef]


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[CrossRef]


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[27] G. Bradski, "The OpenCV Library," Doctor Dobb's Journal of Software Tool, vol. 25 (11), pp. 120-126

[28] A. V. S. Vempati, A. Zisserman and C. V. Jawahar, "Generalized RBF feature maps for efficient detection," in Proc. British Machine Vision Conference, pp. 2.1-2.11, 2010.
[CrossRef] [SCOPUS Times Cited 60]






References Weight

Web of Science® Citations for all references: 12,021 TCR
SCOPUS® Citations for all references: 6,046 TCR

Web of Science® Average Citations per reference: 401 ACR
SCOPUS® Average Citations per reference: 202 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-11-28 08:35 in 139 seconds.




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