@article{ref1,
title="Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks",
journal="Computational urban science",
year="2022",
author="Pi, Yalong and Duffield, Nick and Behzadan, Amir H. and Lomax, Tim",
volume="2",
number="1",
pages="2-2",
abstract="Accurate and prompt traffic data are necessary for the successful management of major events. Computer vision techniques, such as convolutional neural network (CNN) applied on video monitoring data, can provide a cost-efficient and timely alternative to traditional data collection and analysis methods. This paper presents a framework designed to take videos as input and output traffic volume counts and intersection turning patterns. This framework comprises a CNN model and an object tracking algorithm to detect and track vehicles in the camera's pixel view first. Homographic projection then maps vehicle spatial-temporal information (including unique ID, location, and timestamp) onto an orthogonal real-scale map, from which the traffic counts and turns are computed. Several video data are manually labeled and compared with the framework output. The following results show a robust traffic volume count accuracy up to 96.91%. Moreover, this work investigates the performance influencing factors including lighting condition (over a 24-h-period), pixel size, and camera angle. Based on the analysis, it is suggested to place cameras such that detection pixel size is above 2343 and the view angle is below 22°, for more accurate counts. Next, previous and current traffic reports after Texas A&M home football games are compared with the framework output. <br><br>RESULTS suggest that the proposed framework is able to reproduce traffic volume change trends for different traffic directions. Lastly, this work also contributes a new intersection turning pattern, i.e., counts for each ingress-egress edge pair, with its optimization technique which result in an accuracy between 43% and 72%.<p /> <p>Language: en</p>",
language="en",
issn="2730-6852",
doi="10.1007/s43762-021-00031-w",
url="http://dx.doi.org/10.1007/s43762-021-00031-w"
}