@article{ref1,
title="Method to estimate pedestrian traffic using convolutional neural network",
journal="Transportation research procedia",
year="2020",
author="Kataev, Georgii and Varkentin, Vitalii and Nikolskaia, Kseniia",
volume="50",
number="",
pages="234-241",
abstract="This study describes a neural network approach to collecting pedestrian traffic statistics from street surveillance cameras. Collecting and processing pedestrian traffic is one of the most important areas in the development of smart cities. To solve the problem of collecting pedestrian traffic statistics, a modern system of object detection in real time, YOLOv3, was used. To train the neural network, a data set of 750 labeled frames with pedestrians was used, which amounted to 20,000 objects. According to the results of the system testing, the recognition accuracy was 79%. The presented data set can be used by other researchers in their studies.<p /> <p>Language: en</p>",
language="en",
issn="2352-1465",
doi="10.1016/j.trpro.2020.10.029",
url="http://dx.doi.org/10.1016/j.trpro.2020.10.029"
}