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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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General Research Article

Performance Evaluation of Edge Orientation Histograms Based System for Real-time Object Detection in Two Separate Platforms

Author(s): Souhail Guennouni*, Anass Mansouri and Ali Ahaitouf

Volume 13, Issue 1, 2020

Page: [86 - 90] Pages: 5

DOI: 10.2174/2213275912666190104122129

Price: $65

Abstract

Background: Real-time object detection has been attracting much attention recently due the increasing market need of such systems. Therefore, different detection algorithms and techniques have been evaluated to create a reliable detection system. The main challenge to implement a realtime reliable detection system relies on the algorithm training phase. During this phase, a large number of object image database needs to be prepared for each object to be detected.

Objective: In this work, we implement a simultaneous object detection system based on local Edge Orientation Histograms (EOH) as feature extraction method with a smaller objects image database. Then, we evaluate the performance of this detection system in two separate platforms.

Methods: We evaluated the performance of the detection of Ede Orientation Histograms against HAAR and Local Binary Patterns (LBP) algorithms using two different objects. After that, we discussed the evaluation of the detection systems on the standard platform in addition to the porting process into the embedded platform.

Results: We achieved excellent results on both face and hands objects using less than 300 samples. This number is really negligible compared to the size of the image database used by state-of-the-art solutions. In terms of quality of detection, we have achieved more than 93% detection accuracy for the standard platform and 91.8% in the embedded platform for both face and hand objects.

Conclusion: In this work, we demonstrated how Edge Orientation Histograms-based detection system gives better performance results than the algorithms evaluated against with less than 300 images database in two separate platforms.

Keywords: Computer vision, object detection, face detection, detection performance, real-time object detection, EOH, LBP, Haar-like.

Graphical Abstract

[1]
S. Guennouni, A. Ahaitouf, and A. Mansouri, "a comparative study of multiple object detection using Haar-like feature selection and local binary patterns in several platforms", Model. Simul. Eng., vol. 2015, . 948960 2015
[http://dx.doi.org/10.1155/2015/948960]
[2]
S. Guennouni, A. Ahaitouf, and A. Mansouri, "Face detection: Comparing Haar-like combined with cascade classifiers and Edge Orientation Matching", In: 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco, 2017, pp. 1-4.
[http://dx.doi.org/10.1109/WITS.2017.7934604]
[3]
A. Tedeschi, A. Liguori, and F. Benedetto, "Information security and threats in mobile appliances", Recent Pat. Comput. Sci., vol. 7, no. 1, pp. 3-11, 2014.
[http://dx.doi.org/10.2174/2213275907666140610200010]
[4]
B. Menser, and F. Muller, "Face detection in color images using principal components analysis", In: In Seventh International Conference on (Conf. Publ. No. 465) Image Processing And Its Applications, Manchester, UK, 1999, pp. 620-624. Vol. 2
[http://dx.doi.org/10.1049/cp:19990397]
[5]
E. Saber, and A.M. Tekalp, "Fontal-view face detection and facial feature extraction using color, shape and symmetry based cost function", Pattern Recognit. Lett., vol. 19, no. 8, pp. 669-680, 1998.
[http://dx.doi.org/10.1016/S0167-8655(98)00044-0]
[6]
E. Saber, "A.M., Takalp, R. Eschbach and K. Knox, “Automatic image annotation using adaptive color classification", Graph. Models Image Proc., vol. 58, pp. 115-126, 1996.
[http://dx.doi.org/10.1006/gmip.1996.0010]
[7]
S.B. Rowley, T. Kanade, and S. Baluja, "Neural network-based face detection", IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 1, pp. 23-38, 1998.
[http://dx.doi.org/10.1109/34.655647]
[8]
P. Viola, and M. Jones, "Rapid object detection using a boosted cascade of simple features", In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, HI, USA, 2001, pp. 511-518.
[http://dx.doi.org/10.1109/CVPR.2001.990517]
[9]
B. Froba, and C. Kublbeck, "Real-time face detection using edge-orientation matching", In: 3rd International Conference on Audio and Video Based Biometric Person Authentication, Halmstad, Sweden, 2001, pp. 78-83.
[http://dx.doi.org/10.1007/3-540-45344-X_12]
[10]
W. Freeman, and M. Roth, Orientation histogram for hand gesture recognition., IEEE Intl. Wkshp. on Automatic Face and Gesture Recognition: Zurich, 1995.
[11]
A. Tedeschi, and F. Benedetto, "A real-time automatic pavement crack and pothole recognition system for mobile Android-based devices", Adv. Eng. Inform., vol. 32, pp. 11-25, 2017.
[http://dx.doi.org/10.1016/j.aei.2016.12.004]
[12]
T. Sim, S. Baker, and M. Bsat, "The CMU Pose, Illumination, and Expression (PIE) database", In: Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, Washington, DC, USA, 2002, pp. 53-58.
[13]
K. Levi, and Y. Weiss, "Learning object detection from a small number of examples: the importance of good features", In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 2004, pp. 53-60.Vol. 2,
[http://dx.doi.org/10.1109/CVPR.2004.1315144]
[14]
" M.I.T. Faces Database. Available from:", http://web.mit.edu/emeyers/www/face_ databases.html

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