Detection and tracking people in real-time with YOLO object detector-Reference-Cited by-同舟云学术

Detection and tracking people in real-time with YOLO object detector

Published:2020-04-29 Issue: Volume: Page:69-75
ISSN:2707-9481
Container-title:Challenges of Science
language:en
Short-container-title:

Author:

Srazhdinova Aziza¹^ORCID,Ahmetova Asel’²^ORCID,Anvarov Sunvar³^ORCID

Affiliation:

1. Al-Farabi Kazakh National University, Kazakhstan E-mail: aziza0167@gmail.com ORCID ID 0000-0003-1963-0005

2. Al-Farabi Kazakh National University, Kazakhstan E-mail: baltabekova_1994@ mail.ru ORCID ID 0000-0002-7718-6478

3. Al-Farabi Kazakh National University, Kazakhstan E-mail: anvarov.sunvar@gmail.com ORCID ID 0000-0002-8330-0716

Abstract

In this article, we wrote not a large program to solve tasks for detection and tracking objects in real-time. The program was written in Python programming language. For object detection, a convolutional neural network was used with YOLOV3 architecture. A preliminary analysis was carried out of several variations of YOLO with CNN models. In the article, we justify why we want to use YOLO, and what it is and how to use and process the model output. We will also present the code in the form of a flowchart and as a result of the program's performance, we will show a picture of the program's operation in real-time, which was launched at one of the live lectures at the University.

Publisher

Institute of Metallurgy and Ore Benefication

Subject

General Environmental Science

Link

http://kims-imio.kz/wp-content/uploads/2019/04/2020_010.pdf

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