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Stefan cel Mare
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Print ISSN: 1582-7445
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WorldCat: 643243560
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: 1,204 | Views: 3,838

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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Full text preview
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

[1] P. Dollar, C. Wojek, B. Schiele, and P. Perona, "Pedestrian Detection: An Evaluation of the State of the Art," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 743-761, Apr. 2012.
[CrossRef] [Web of Science Times Cited 2105] [SCOPUS Times Cited 2711]


[2] J. L. Raheja, S. Deora, and A. Chaudhary, "Cross border intruder detection in hilly terrain in dark environment," Optik - International Journal for Light and Electron Optics, vol. 127, no. 2, pp. 535-538, Jan. 2016.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 10]


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[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 9]


[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 9] [SCOPUS Times Cited 11]


[5] J.-L. Chua, Y. C. Chang, and W. K. Lim, "A simple vision-based fall detection technique for indoor video surveillance," Signal, Image and Video Processing, vol. 9, no. 3, pp. 623-633, Mar. 2015.
[CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 111]


[6] C. Q. Lai and S. S. Teoh, "A review on pedestrian detection techniques based on Histogram of Oriented gradient feature," in Proc. IEEE Student Conference on Research and Development (SCOReD), 2014, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 11]


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[CrossRef] [SCOPUS Times Cited 33]


[8] G. Zheng and Y. Chen, "A review on vision-based pedestrian detection," in Proc. IEEE Global High Tech Congress on Electronics (GHTCE), 2012, pp. 49-54.
[CrossRef] [SCOPUS Times Cited 20]


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[CrossRef] [Web of Science Times Cited 157] [SCOPUS Times Cited 216]


[10] Y. Mu, S. Yan, Y. Liu, T. Huang, and B. Zhou, "Discriminative local binary patterns for human detection in personal album," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
[CrossRef] [SCOPUS Times Cited 293]


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[CrossRef]


[14] F. Porikli, "Integral histogram: a fast way to extract histograms in Cartesian spaces," in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, pp. 829-836 vol. 1.
[CrossRef] [Web of Science Times Cited 381] [SCOPUS Times Cited 631]


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[CrossRef] [Web of Science Times Cited 8794]


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[CrossRef] [SCOPUS Times Cited 24]


[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 1032] [SCOPUS Times Cited 1544]


[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 41] [SCOPUS Times Cited 52]


[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 18] [SCOPUS Times Cited 21]


[20] M. Hemmati, M. Biglari-Abhari, S. Berber, and S. Niar, "HOG Feature Extractor Hardware Accelerator for Real-Time Pedestrian Detection," in Proc. 17th Euromicro Conference on Digital System Design (DSD), 2014, pp. 543-550.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 30]


[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 65] [SCOPUS Times Cited 75]


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[23] I. Jolliffe, "Principal component analysis," Wiley StatsRef: Statistics Reference Online, 2002.
[CrossRef]


[24] V. N. Vapnik, "The nature of statistical learning theory", Springer-Verlag New York, Inc., 1995.
[CrossRef]


[25] S. Abe, "Support Vector Machines for Pattern Classification" Advances in Pattern Recognition, Springer-Verlag New York, Inc., 2005.
[CrossRef]


[26] C.-C. Hsu, and C.-W. Lin, "A Practical Guide to Support Vector Classification," Department of Computer Science, National Taiwan University, Taipei 106, Taiwan 2003.

[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 71]






References Weight

Web of Science® Citations for all references: 13,322 TCR
SCOPUS® Citations for all references: 6,690 TCR

Web of Science® Average Citations per reference: 444 ACR
SCOPUS® Average Citations per reference: 223 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 2024-11-10 01:08 in 162 seconds.




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