Real Time Traffic Density Measurement using Computer Vision and Dynamic Traffic Control

Chowdhury, Md. Fahim; Biplob, Md. Ryad Ahmed; Uddin, Jia

doi:10.1109/iciev.2018.8641039

Cited by 19 publications

(9 citation statements)

References 11 publications

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“…This method uses predefined object features to detect vehicles, which makes it only capable of detecting objects with similar shapes, and it will be influenced by complex situations such as traffic congestion and occlusion. In Chowdhury et al, moving objects are extracted by identifying the foreground of the video input ( 7 ). Centroids of detected objects are then used from successive frames to form object trajectories.…”

Section: Literature Reviewmentioning

confidence: 99%

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Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach

Jiao

Wang

2022

Transportation Research Record: Journal of the Transportation R

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Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.

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Section: Literature Reviewmentioning

confidence: 99%

“…For real-time traffic data acquisition, different kinds of sensor are used to measure traffic volume, speed, and congestion levels ( 5 , 7 – 9 ). Arduino and infrared radiation (IR) sensors are used to monitor congestion levels, and GPS is used to obtain vehicle trajectories ( 10 – 12 ).…”

mentioning

confidence: 99%

Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach

Jiao

Wang

2022

Transportation Research Record: Journal of the Transportation R

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“…Ряд работ посвящен применению компьютерного зрения в целях повышения пропускной способности перекрестков [9][10][11][12].…”

Section: вд шепелев зв альметова ад моор ви берстеневаunclassified

“…Группой исследователей Chowdhury M.F., Ryad Ahmed Biplob M. и Uddin J. разработана динамическая система управления трафиком, использующая компьютерное зрение, с помощью которого собираются данные о дорожном трафике между двумя перекрестками [11].…”

Section: вд шепелев зв альметова ад моор ви берстеневаunclassified

Performance Optimization of Adaptive Traffic Lights Using Machine Vision

Shepelev¹,

Almetova²,

Moor³

et al. 2020

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“…Исследователи [12] также разработали динамическую систему управления трафиком, которая с помощью компьютерного зрения собирает данные о дорожном трафике только между двумя перекрестками.…”

Section: Introductionunclassified

Predicting and Managing Lane Capacity at a Signal-Controlled Intersection

Shepelev¹,

Gorodokin²,

Slobodin³

et al. 2021

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Real Time Traffic Density Measurement using Computer Vision and Dynamic Traffic Control

Cited by 19 publications

References 11 publications

Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach

Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach

Performance Optimization of Adaptive Traffic Lights Using Machine Vision

Predicting and Managing Lane Capacity at a Signal-Controlled Intersection

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