Click to open the HelpDesk interface
AECE - Front page banner



JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: May 2020
Next issue: Aug 2020
Avg review time: 69 days


Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


2,637,343 unique visits
Since November 1, 2009

Robots online now


SCImago Journal & Country Rank


Anycast DNS Hosting

 Volume 20 (2020)
     »   Issue 2 / 2020
     »   Issue 1 / 2020
 Volume 19 (2019)
     »   Issue 4 / 2019
     »   Issue 3 / 2019
     »   Issue 2 / 2019
     »   Issue 1 / 2019
 Volume 18 (2018)
     »   Issue 4 / 2018
     »   Issue 3 / 2018
     »   Issue 2 / 2018
     »   Issue 1 / 2018
 Volume 17 (2017)
     »   Issue 4 / 2017
     »   Issue 3 / 2017
     »   Issue 2 / 2017
     »   Issue 1 / 2017
 Volume 16 (2016)
     »   Issue 4 / 2016
     »   Issue 3 / 2016
     »   Issue 2 / 2016
     »   Issue 1 / 2016
  View all issues  


Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
Issue 2/2018



Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

Starting on the 15th of December 2019 all paper authors are required to enter their SCOPUS IDs. You may use the free SCOPUS ID lookup form to find yours in case you don't remember it.

Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

Read More »


  4/2016 - 16
View TOC | « Previous Article | Next Article »


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
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,987 KB) | Citation | Downloads: 532 | Views: 1,985

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

Quick view
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 1299] [SCOPUS Times Cited 1735]

[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 5] [SCOPUS Times Cited 4]

[3] J. E. Mallah, F. Carrino, O. A. Khaled, and E. Mugellini, "Crowd Monitoring," in Distributed, Ambient, and Pervasive Interactions, vol. 9189, N. Streitz and P. Markopoulos, Eds. Cham: Springer International Publishing, 2015, pp. 496-505.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 7]

[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 6] [SCOPUS Times Cited 7]

[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 54] [SCOPUS Times Cited 60]

[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 8]

[7] B. Li, Q. Yao, and K. Wang, "A review on vision-based pedestrian detection in intelligent transportation systems," in Proc. 9th IEEE International Conference on Networking, Sensing and Control (ICNSC), 2012, pp. 393-398.
[CrossRef] [SCOPUS Times Cited 24]

[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 12]

[9] D. T. Nguyen, W. Li, and P. O. Ogunbona, "Human detection from images and videos: A survey," Pattern Recognition, vol. 51, pp. 148-175, March 2016.
[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 87]

[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 244]

[11] A. Mohan, C. Papageorgiou, and T. Poggio, "Example-Based Object Detection in Images by Components," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 349-361, Apr. 2001.
[CrossRef] [Web of Science Times Cited 552] [SCOPUS Times Cited 738]

[12] O. Biglari, R. Ahsan, and M. Rahi, "Human Detection Using SURF and SIFT Feature Extraction Methods in Different Color Spaces," Journal of Mathematics and Computer Science, vol. 11, p. 111, 2014.

[13] N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, pp. 886-893 vol 1.

[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 342] [SCOPUS Times Cited 581]

[15] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, pp. I-511-I-518 vol.1.
[CrossRef] [Web of Science Times Cited 6167]

[16] Y. Said, M. Atri, and R. Tourki, "Human detection based on integral Histograms of Oriented Gradients and SVM," in Proc. International Conference on Communications, Computing and Control Applications (CCCA), 2011, pp. 1-5.
[CrossRef] [SCOPUS Times Cited 18]

[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 744]

[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 32] [SCOPUS Times Cited 39]

[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 9] [SCOPUS Times Cited 11]

[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 18] [SCOPUS Times Cited 21]

[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 46] [SCOPUS Times Cited 50]

[22] I. Fodor, "A Survey of Dimension Reduction Techniques," technical report, Lawrence Livermore National Lab., CA (US), 2002.

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

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

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

[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 53]

References Weight

Web of Science® Citations for all references: 9,342 TCR
SCOPUS® Citations for all references: 3,699 TCR

Web of Science® Average Citations per reference: 311 ACR
SCOPUS® Average Citations per reference: 123 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 2020-08-12 01:00 in 165 seconds.

Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2020
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.

Website loading speed and performance optimization powered by: