


Volume 20 No 16 (2022)
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Vehicle-Pedestrian Detection Methods for Urban Traffic Control System: A Survey
Karnavi Desai , Dr. Pooja Gupta , PrashantSahatiya
Abstract
The increase in congestion on traffic lanes is a major problem hindering the development of an urban city.
The reason for this is the increasing number of vehicles on roads leading to large time delays on traffic
intersections. To overcome this problem and to make traffic control systems dynamic, several methods
and techniques have been introduced throughout the years. The static traffic control systems worked on
fixed timings which were allocated to each traffic lane and were not able to be altered. Also, there was no
provision for counting and detection of pedestrians on the zebra crossings as well as the detection of
emergency vehicles in traffic. We will explore several machine learning and deep learning models for the
detection of vehicles and pedestrians in this review article, evaluate their viability in terms of cost,
dependability, accuracy, and efficiency, and add some new features to improve the performance of the
current system
Keywords
Traffic Control System, Object Detection, Pedestrian Detection, You Only Look Once (YOLO), Convolutional Neural Network, Deep-SORT, Artificial Neural Network, Deep-Q Network, Simulation of Urban Mobility, Reinforcement Learning.
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