Qalaai Zanist Scientific Journal
گۆڤارى قەڵاى زانست

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ISSN 2518-6558 (Print)
Qalaai Zanist Scientific Journal  
Volume 2, Issue 2, April 2017

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

 
Paper Title: Fast and Accurate Real Time Pedestrian Detection Using Convolutional Neural Network
Author (s): Hayder Albehadili
Laith Alzubaidi
Jabbar Rashed
Murtadha Al-Imam
Haider A. Alwzwazy
https://doi.org/10.25212/lfu.qzj.2.2.29
   
Index Terms: Convolutional Neural Network, Pedestrian Detection, Multiscale input images
Abstract: Recently, pedestrian detection has become an important problem of interest. Our work primarily depends on robust and fast deep neural network architectures. This paper used very efficient and recent methods for pedestrian detection. Recently, pedestrian detection has become an important problem of interest. This paper suggests robust convolutional neural network models to solve this problem. We primarily evaluate accuracy and speed. Our work primarily depends on robust and fast deep neural network architectures; substantial changes to those models achieve results that are competitive with prior state-of-the-art methods. As a result, we outperformed all the prior state-of-the-art pedestrian detection methods. We also overtook other models that use extra information during testing and training. All experiments used three pedestrian detection challenge benchmarks: Caltech-USA, INRIA, and ETH.
   
Cite This Paper (APA): Albehadili, H., Alzubaidi, L., Rashed, J., Al-Imam, M., & A. Alwzwazy, H. (2017). Fast and Accurate Real Time Pedestrian Detection Using Convolutional Neural Network. Qalaai Zanist Scientific Journal, 2(2). doi:10.25212/lfu.qzj.2.2.29
Text Language: English
Pp.: 286 - 296
Full Text:
 
 


INVITATION

Researchers and readers of Qalaai zanist are invited to submit their articles with Kurdish, Arabic or English language (to qalaai-zanist@lfu.edu.krd) that are consistent with the objective of this journal for publishing in the future issues.

 
 
 
 
 
 

A Scientific Quarterly Refereed Journal, Published by Lebanese French University (LFU) , Erbil - Kurdistan, Iraq
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