research-article

Fast pedestrian detection based on region of interest and multi-block local binary pattern descriptors

Authors:

Aminou Halidou,

Mahamadou Hamidine,

Roger Atsa Etoundi,

Laye Hadji Diakite,

SouleimanouAuthors Info & Claims

Computers and Electrical Engineering, Volume 40, Issue 8

Pages 375 - 389

https://doi.org/10.1016/j.compeleceng.2014.10.003

Published: 01 November 2014 Publication History

Abstract

Display Omitted We introduced a new ROI method, it decreases computational cost and improve detection speed.We improved the images edges within the candidate region using non-tensor product wavelet filter.We used full-body descriptor based on shapelet and body-part descriptor based on MB-LBP for features extraction.We built a cascade ILFDA classifier to handle large data. Nowadays pedestrian detection plays a crucial role in image or video retrieval, video monitoring systems and driving assistance systems. Detecting moving pedestrians is a challenging task, some of the detection methods are ineffective and slow. Occlusion, rotation, changes in object shapes, real time detection and illumination conditions are predominant obstacles. This paper is focus on the implementation of an efficient and speedy detector. A detection framework based on region of interest (ROI), full-body descriptor, body-part descriptors, and cascade classifier is proposed. ROI identifies, locates, and extracts candidate regions containing pedestrians, thus reducing the number of detection windows. In relation to human detection, independent information sources such as shapelet features and multi-block local binary pattern (MB-LBP) are used for features extraction. Experimental results showed that the proposed-model performs better than some state-of-the-art approaches, with suitable processing time for further operations such as tracking and imminent danger estimation.

References

[1]

U. Franke, I. Kutzbach, Fast stereo based object detection for stop & go traffic, in: Intelligent vehicles symposium proceedings, IEEE, 1996, pp. 339-344.

[2]

B. Heisele, C. Woehler, Motion-based recognition of pedestrians, in: Fourteenth international conference of pattern recognition, vol. 2, IEEE, 1998, pp. 1325-1330.

Digital Library

[3]

C. Papageorgiou, M. Oren, T. Poggio, A general framework for object detection, in: Sixth international conference of computer vision, IEEE, 1998, pp. 555-562.

Digital Library

[4]

P. Viola, M. Jones, Robust real-time object detection, International Journal of Computer Vision (2004).

Digital Library

[5]

N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Computer society conference of computer vision and pattern recognition, vol. 1, IEEE, 2005, pp. 886-893.

Digital Library

[6]

O. Tuzel, F. Porikli, P. Meer, Region covariance: a fast descriptor for detection and classification, Comput Vision (2006) 589-600.

Digital Library

[7]

P. Sabzmeydani, G. Mori, Detecting pedestrians by learning shapelet features, in: Conference of computer vision and pattern recognition, IEEE, 2007, pp. 1-8.

[8]

O. Tuzel, F. Porikli, P. Meer, Pedestrian detection via classification on riemannian manifolds, Pattern Anal Mach Intell Trans, 30 (2008) 1713-1727.

Digital Library

[9]

Y. Mu, S. Yan, Y. Liu, T. Huang, B. Zhou, Discriminative local binary patterns for human detection in personal album, in: Conference of computer vision and pattern recognition, IEEE, 2008, pp. 1-8.

[10]

S. Paisitkriangkrai, C. Shen, J. Zhang, Fast pedestrian detection using a cascade of boosted covariance features, Circ Syst Video Technol Trans, 18 (2008) 1140-1151.

Digital Library

[11]

B. Wu, R. Nevatia, Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors, in: Tenth international conference of computer vision, vol. 1, IEEE, 2005, pp. 90-97.

Digital Library

[12]

P. Doll�r, C. Wojek, B. Schiele, P. Perona, Pedestrian detection: an evaluation of the state of the art, Pattern Anal Mach Intell Trans, 34 (2012) 743-761.

Digital Library

[13]

D.M. Gavrila, V. Philomin, Real-time object detection for smart vehicles, in: Seventh international conference of computer vision, vol. 1, IEEE, 1999, pp. 87-93.

[14]

P.F. Felzenszwalb, Learning models for object recognition, in: Computer society conference of computer vision and pattern recognition, vol. 1, IEEE, 2001, pp. I-1056.

[15]

B. Leibe, E. Seemann, B. Schiele, Pedestrian detection in crowded scenes, in: Computer society conference of computer vision and pattern recognition, vol. 1, IEEE, 2005, pp. 878-885.

Digital Library

[16]

Q. Zhu, M.-C. Yeh, K.-T. Cheng, S. Avidan, Fast human detection using a cascade of histograms of oriented gradients, in: Computer society conference of computer vision and pattern recognition, vol. 2, IEEE, 2006, pp. 1491-1498.

Digital Library

[17]

S. Munder, D. Gavrila, An experimental study on pedestrian classification, Pattern Anal Mach Intell Trans, 28 (2006) 1863-1868.

Digital Library

[18]

E. Seemann, M. Fritz, B. Schiele, Towards robust pedestrian detection in crowded image sequences, in: Conference of computer vision and pattern recognition, IEEE, 2007, pp. 1-8.

[19]

S. Maji, A.C. Berg, J. Malik, Classification using intersection kernel support vector machines is efficient, in: Conference of computer vision and pattern recognition, IEEE, 2008, pp. 1-8.

[20]

Y. Xu, X. Cao, H. Qiao, An efficient tree classifier ensemble-based approach for pedestrian detection, Syst Man Cybernet Part B: Cybernet Trans, 41 (2011) 107-117.

Digital Library

[21]

D. Thanh Nguyen, P.O. Ogunbona, W. Li, A novel shape-based non-redundant local binary pattern descriptor for object detection, Pattern Recognit (2013).

Digital Library

[22]

P. Viola, M. Jones, D. Snow, Detecting pedestrians using patterns of motion and appearance, Int J Comput Vision, 63 (2005) 153-161.

Digital Library

[23]

A. Halidou, X. You, Fast pedestrian detection using bwlsd for roi, in: Wireless and optical communication conference (WOCC), 2013 22nd, IEEE, 2013, pp. 610-615.

[24]

A. Halidou, X. You, B. Bogno, Pedestrian detection based on multi-block local binary pattern and biologically inspired feature, Comput Inform Sci, 7 (2014) 125.

[25]

K. Guo, P. Ishwar, J. Konrad, Action recognition from video using feature covariance matrices, Image Processing Transactions, 22 (2013) 2479-2494.

Digital Library

Cited By

Ferhat RChelali F(2024)Textural feature descriptors for a static and dynamic hand gesture recognition systemMultimedia Tools and Applications10.1007/s11042-023-15410-083:3(8165-8187)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15410-0
Hashemzadeh MFarajzadeh N(2019)Combining keypoint-based and segment-based features for counting people in crowded scenesInformation Sciences: an International Journal10.1016/j.ins.2016.01.060345:C(199-216)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2016.01.060
Sengar SMukhopadhyay S(2017)Motion detection using block based bi-directional optical flow methodJournal of Visual Communication and Image Representation10.5555/3163595.316379749:C(89-103)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.5555/3163595.3163797
Show More Cited By

Fast pedestrian detection based on region of interest and multi-block local binary pattern descriptors
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Machine learning
    1. Machine learning algorithms

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Published In

cover image Computers and Electrical Engineering

Computers and Electrical Engineering Volume 40, Issue 8

November 2014

408 pages

ISSN:0045-7906

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 November 2014

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Cited By

Ferhat RChelali F(2024)Textural feature descriptors for a static and dynamic hand gesture recognition systemMultimedia Tools and Applications10.1007/s11042-023-15410-083:3(8165-8187)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15410-0
Hashemzadeh MFarajzadeh N(2019)Combining keypoint-based and segment-based features for counting people in crowded scenesInformation Sciences: an International Journal10.1016/j.ins.2016.01.060345:C(199-216)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2016.01.060
Sengar SMukhopadhyay S(2017)Motion detection using block based bi-directional optical flow methodJournal of Visual Communication and Image Representation10.5555/3163595.316379749:C(89-103)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.5555/3163595.3163797
Chakraborty SSingh SChakraborty P(2017)Local quadruple patternComputers and Electrical Engineering10.1016/j.compeleceng.2017.06.01362:C(92-104)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.compeleceng.2017.06.013
Chen HZhao XSun STan M(2017)PLS-CCA heterogeneous features fusion-based low-resolution human detection method for outdoor video surveillanceInternational Journal of Automation and Computing10.1007/s11633-016-1029-814:2(136-146)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s11633-016-1029-8

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