Fast and Accurate Real Time Pedestrian Detection Using Convolutional Neural Network

Hayder Albehadili; Laith Alzubaidi; Jabbar Rashed; Murtadha Al-Imam; Haider A. Alwzwazy

doi:10.25212/lfu.qzj.2.2.29

المؤلفون

Hayder Albehadili Department of Software , Kadhum College for Islamic Science University, Iraq
Laith Alzubaidi Department of Systems and Applications, University of Information Technology & Communications Baghdad, Iraq
Jabbar Rashed Department of Electrical Engineering , Engineering College, University of Misan- Iraq
Murtadha Al-Imam Department of Software , Kadhum College for Islamic Science University, Iraq
Haider A. Alwzwazy Department of Mathematics, Mathematics College, University of Misan- Iraq

DOI:

https://doi.org/10.25212/lfu.qzj.2.2.29

الكلمات المفتاحية:

Convolutional Neural Network, Pedestrian Detection, Multiscale input images

الملخص

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.

التنزيلات

بيانات التنزيل غير متوفرة بعد.

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