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Review of Computer Engineering Research

June 2020, Volume 7, 1, pp 38-46

Smart Feature Fusion and Model for Human Detection

Htet Htet Lin

Htet Htet Lin 1

  1. Assistant Lecturer, Faculty of Computer Software Computer University (BanMaw), Myanmar. 1

on Google Scholar
on PubMed

Pages: 38-46

DOI: 10.18488/journal.76.2020.71.38.46

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Article History:

Received: 23 January, 2020
Revised: 26 February, 2020
Accepted: 30 March, 2020
Published: 08 May, 2020


Extraction of discriminate and accurate features is challenging to precise the statistical data on monitoring people. It still remains an active research due to various variations as inter class and intra class, lighting challenge, static and dynamic occlusion. To tackle this variation and occlusion issue, this paper proposes to combine the differential gradient and statistical Tamura features with joint histogram. In addition, these extracted smart features use actually to detect people by using the gradient feature descriptor and a statistical feature detector. The model fusion of human detection creates by combining two models result (Grammar model and Poselet model) with the adaptive threshold weighted non-maximum suppression algorithm. The system presents a powerful fusion insight to capture the stronger occlusion parts and several variations of the foreground people. To compare the performance with the state of the arts, the public Pascal VOC 2007 Dataset is used. The outperformed result of this work proofs our concern.
Contribution/ Originality
Various feature and model was studied to detect the human. Focused on this, a fusion feature and model was contributed.


G feature, T feature, Grammar Mode, Poselet Model, Smart feature fusion, Smart model fusion


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[2]  N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, pp. 886-893.

[3]  Q. Zhu, S. Avidan, M. Yeh, and K. Cheng, "Fast human detection using a cascade of histograms of oriented gradients," in In Proceeding IEEE International Conference on Computer Vision and Pattern Recognition, 2006, pp. 1491–1498.

[4]  Y. Mu, S. Yan, Y. Liu, T. Huang, and B. Zhou, "Discriminative local binary patterns for human detection in personal album," in In Proceeding IEEE International Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8.

[5]  P. Viola, M. Jones, and D. Snow, "Detecting pedestrians using patterns of motion and appearance," International Journal of Computer Vision vol. 63, pp. 153–161, 2005.

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[8]  Y. Jiang and J. Ma, "Combination features and models for human detection," in In Proceeding IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

[9]  H. H. Lin and K. T. Win, "Person detection based on fusion histogram of gradients with texture (FHGT) local features”, in Researchscript," International Journal of Research in Computer Scientific (IJRCS), vol. 5, pp. 1 – 4, 2018.

[10]X. Ren and D. Ramanan, "Histograms of sparse codes for object detection," in In Proceeding IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.


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This study received no specific financial support.

Competing Interests:

The author declares that there are no conflicts of interests regarding the publication of this paper.


The author wishes to thank her parents, elder brother and relatives whose inspirations make their work hard and who are always willing to give the authors all moral support.

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