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200330

Deep Convolutional Neural Network based Person Detection and People Counting System

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Last updated: 23 Jan 2023

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Abstract

Nowadays, computer vision is an actively developing and one of the most important part of Artificial Intelligence. There are a lot of works in this area. Computer Vision has a wide range of uses. For example, in medicine it can be used for diagnosing a Magnetic Resonance Imaging or X-ray image, in security systems - for detecting intruders, for driverless cars or robotics to navigate in space, etc. However, often image recognition works together with object detection. Usually required object for recognition takes only the small part of original image whereas the rest part of the image does not carry useful information for recognizing. By this reason to optimize computation time, we need to find this object, bef ore recognizing. There are various methods and technologies for object detection in the image. This work demonstrates one of the actual method in computer vision which named Deep Convolutional Neural Networks and shows the advantages of this method in person detection and people counting.

DOI

10.21608/aeta.2018.200330

Keywords

Object detection, Neural Networks, deep cnn, Machine Learning, Computer Vision

Authors

First Name

Maksat

Last Name

Kanatov

MiddleName

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Affiliation

Kazakh-National Research Technical University after K.I.Satbayev, Almaty, Kazakhstan

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Orcid

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First Name

Lyazzat

Last Name

Atymtayeva

MiddleName

-

Affiliation

Suleyman Demirel University, Kaskelen, Kazakhstan

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Volume

7

Article Issue

3

Related Issue

28263

Issue Date

2018-09-01

Receive Date

2021-10-19

Publish Date

2018-09-01

Page Start

5

Page End

9

Print ISSN

2090-9535

Online ISSN

2090-9543

Link

https://aeta.journals.ekb.eg/article_200330.html

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https://aeta.journals.ekb.eg/service?article_code=200330

Order

200,330

Type

Original Article

Type Code

2,017

Publication Type

Journal

Publication Title

Advanced Engineering Technology and Application

Publication Link

https://aeta.journals.ekb.eg/

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Article

Created At

23 Jan 2023