|4/2016 - 16|
An Efficient Method of HOG Feature Extraction Using Selective Histogram Bin and PCA Feature ReductionLAI, C. Q. , TEOH, S. S.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,987 KB) | Citation | Downloads: 449 | Views: 1,584|
feature extraction, image analysis, object detection, pattern recognition, computer vision
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
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|
| 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 1159] [SCOPUS Times Cited 1524]
 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]
 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 2] [SCOPUS Times Cited 6]
 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]
 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 45] [SCOPUS Times Cited 52]
 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 5]
 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 21]
 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 11]
 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 49] [SCOPUS Times Cited 64]
 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 229]
 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 502] [SCOPUS Times Cited 722]
 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.
 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.
 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 277] [SCOPUS Times Cited 570]
 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.
 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 15]
 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 685]
 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 31] [SCOPUS Times Cited 38]
 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 3] [SCOPUS Times Cited 4]
 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 15] [SCOPUS Times Cited 19]
 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 39] [SCOPUS Times Cited 42]
 I. Fodor, "A Survey of Dimension Reduction Techniques," technical report, Lawrence Livermore National Lab., CA (US), 2002.
 I. Jolliffe, "Principal component analysis," Wiley StatsRef: Statistics Reference Online, 2002.
 V. N. Vapnik, "The nature of statistical learning theory", Springer-Verlag New York, Inc., 1995.
 S. Abe, "Support Vector Machines for Pattern Classification" Advances in Pattern Recognition, Springer-Verlag New York, Inc., 2005.
 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.
 G. Bradski, "The OpenCV Library," Doctor Dobb's Journal of Software Tool, vol. 25 (11), pp. 120-126
 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 51]
Web of Science® Citations for all references: 2,818 TCR
SCOPUS® Citations for all references: 3,384 TCR
Web of Science® Average Citations per reference: 94 ACR
SCOPUS® Average Citations per reference: 113 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 2019-12-04 15:13 in 166 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.
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.