Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Smart System of a Real-Time Pedestrian Detection for Smart City. / Ali Muthanna, Mohammed Saleh; Lyachek, Yuliy T.; Obadi Musaeed, Abdulfattah Mohammed; Ahmed Hazzaa Esmail, Yaqoob; Adam, Abuzar B.M.
Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020. ред. / S. Shaposhnikov. Institute of Electrical and Electronics Engineers Inc., 2020. стр. 45-50 9039333 (Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Smart System of a Real-Time Pedestrian Detection for Smart City
AU - Ali Muthanna, Mohammed Saleh
AU - Lyachek, Yuliy T.
AU - Obadi Musaeed, Abdulfattah Mohammed
AU - Ahmed Hazzaa Esmail, Yaqoob
AU - Adam, Abuzar B.M.
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - This paper proposes the possibility of recognizing pedestrians in real time on the end devices using a microcomputer to automate the process of signaling traffic lights and improve system mobility. The paper is devoted to a brief analysis of existing systems for the detection of pedestrians, with the subsequent development of its own system based on those studied, and its further implementation on the Raspberry Pi microcomputer. A natural experiment was conducted to detect pedestrians in an image with the subsequent determination of their location at a pedestrian crossing. A number of popular pedestrian detection systems were analyzed in terms of speed, accuracy of determination, and the possibility of implementation on microcomputers. Recommendations on the choice of the system, conditions of use and improvement of characteristics. The developed system automates the work of the traffic light through the introduction of additional functions (tracking a pedestrian's posture, moving pedestrians along the roadway), ensuring the safety of road users, which allows to improve the level of the urban environment as a whole.
AB - This paper proposes the possibility of recognizing pedestrians in real time on the end devices using a microcomputer to automate the process of signaling traffic lights and improve system mobility. The paper is devoted to a brief analysis of existing systems for the detection of pedestrians, with the subsequent development of its own system based on those studied, and its further implementation on the Raspberry Pi microcomputer. A natural experiment was conducted to detect pedestrians in an image with the subsequent determination of their location at a pedestrian crossing. A number of popular pedestrian detection systems were analyzed in terms of speed, accuracy of determination, and the possibility of implementation on microcomputers. Recommendations on the choice of the system, conditions of use and improvement of characteristics. The developed system automates the work of the traffic light through the introduction of additional functions (tracking a pedestrian's posture, moving pedestrians along the roadway), ensuring the safety of road users, which allows to improve the level of the urban environment as a whole.
KW - Deep learning
KW - IoT
KW - machine learning
KW - pattern recognition
KW - pedestrian detection
KW - Raspberry Pi
UR - http://www.scopus.com/inward/record.url?scp=85082992960&partnerID=8YFLogxK
U2 - 10.1109/EIConRus49466.2020.9039333
DO - 10.1109/EIConRus49466.2020.9039333
M3 - Conference contribution
AN - SCOPUS:85082992960
T3 - Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
SP - 45
EP - 50
BT - Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
A2 - Shaposhnikov, S.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
Y2 - 27 January 2020 through 30 January 2020
ER -
ID: 87324746