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.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
EditorsS. Shaposhnikov
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages45-50
Number of pages6
ISBN (Electronic)9781728157610
DOIs
StatePublished - Jan 2020
Event2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020 - St. Petersburg and Moscow, Russian Federation
Duration: 27 Jan 202030 Jan 2020

Publication series

NameProceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020

Conference

Conference2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
Country/TerritoryRussian Federation
CitySt. Petersburg and Moscow
Period27/01/2030/01/20

    Research areas

  • Deep learning, IoT, machine learning, pattern recognition, pedestrian detection, Raspberry Pi

    Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Information Systems and Management
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

ID: 87324746