Determining vehicle speed based on video using convolutional neural network

Grents, Alexander; Varkentin, Vitalii; Goryaev, Nikolay

doi:10.1016/j.trpro.2020.10.024

Cited by 29 publications

(11 citation statements)

References 18 publications

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“…The vehicle speed data was collected from the waze data using the website ( https://corona.jakarta.go.id/ ). Several studies have successfully used vehicle speed data as an indicator for traffic jams incidents ( Du et al, 2021 ; Grents et al, 2020 ; Harantová et al, 2020 ). The vehicle speed data were collected from the Jakarta Smart city website ( https://corona.jakarta.go.id/ ).…”

Section: Methodsmentioning

confidence: 99%

RETRACTED: Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

Rendana

Idris

Rahim

et al. 2022

Air, Soil and Water Research

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The large transmission of COVID-19 has resulted in a deep impact on the surrounding urban environments, especially on air quality and traffic flows. The objective of this study was to analyze air pollutant concentrations (PM10, SO2, NO2, CO, and O3) and traffic volumes at five congested districts (Bundaran HI, Kelapa Gading, Jagakarsa, Lubang Buaya, and Kebon Jeruk) within Jakarta city impacted by the large-scale social restriction (LSSR) policy. Air quality data during three periods; before, during, and after the LSSR at five observed districts was obtained from the Department of Environment of Jakarta using the Air Quality Monitoring (AQMS) tool. While vehicle speed data were obtained from the waze data during the study period. In this study, air pollutant data during three periods; before, during, and after the LSSR were compared with vehicle speed and meteorological data using a statistical analysis. Results revealed the mean traffic volume at all five districts has greatly reduced by 19% from before to during the LSSR period. It was consistent with the mean PM10, NO2, CO, and SO2 concentrations which also dropped about 46%, 45%, 30%, and 23% respectively. In contrast, the concentrations of air pollutants significantly increased after the LSSR period. During the LSSR period, the traffic volume was negatively associated with the O3 concentration ( r = −.86, p < .01), it was different with before the LSSR periods where the traffic volume associated with CO ( r = .88, p < .01) and NO2 ( r = .89, p < .01). The broad analysis of changes in air pollutants and traffic volumes can be used by the authorities to arrange a good air quality management and an effective way for current and future scenarios.

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Section: Methodsmentioning

confidence: 99%

RETRACTED: Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

Rendana

Idris

Rahim

et al. 2022

Air, Soil and Water Research

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“…In addition to the background segmentation techniques mentioned above, recently, some researchers used the Convolutional Neural Networks (CNNs) approach that is able to precisely detect vehicles present in a single static video frame. This method is based on Machine Learning to analyzing a database and classifies and detects objects by searching for features [16]- [19]. Optical flow technique is also widely used by researchers.…”

Section: A Vehicle Detectionmentioning

confidence: 99%

“…Accordingly, the distance (in metric unit) between the entry and departure lines for this area is known. However, based on the video frame rate and the number of frames taken by the vehicle traveled through this ROI, the algorithm calculates the time and thus the speed [8]- [11], [13], [14], [16], [17], and [20].…”

Section: Speed Calculationmentioning

confidence: 99%

Modeling the Estimation Errors of Visual-based Systems Developed for Vehicle Speed Measurement

Alkherret¹,

Abuaddous²

2021

IJACSA

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This paper aims to modeling the relationship between the error of visual-based systems developed for vehicle speed estimation (as dependent variable) and each of the detection region length, the camera angle, and the volume-tocapacity ratio (V/C), as independent variables. Simulation software (VISSIM) is used to generate a set of video clips of predefined traffic based on different values of the dependent variables. These videos are analyzed with a video-based detection and tracking model (VBDATM) developed in 2015. Errors are expressed as differences between each of the actual speeds generated by VISSIM and the speeds computed by the VBDATM divided by the actual speed. The results conducted by the forward stepwise regression analysis show that the V/C ratio does not affect the accuracy of the estimate and there are weak relationships between the estimation error and each of camera position and the detection region length.

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“…To test the proposed method for vehicle classification, a vehicle-feature-based virtual detection zone and virtual detection line, which are predefined for each frame in a video, are used for vehicle feature computation [21]. Grents et al [22] proposed a video-based system that uses a convolutional neural network to count vehicles, classify vehicles, and determine the vehicle speed. Tabassum et al [23,24] applied YOLO and a transfer learning approach to recognize native vehicles and vehicle classification on Bangladeshi Roads.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

Lin

Jeng

Lioa

2021

Mathematical Problems in Engineering

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In recent years, vehicle detection and classification have become essential tasks of intelligent transportation systems, and real-time, accurate vehicle detection from image and video data for traffic monitoring remains challenging. The most noteworthy challenges are real-time system operation to accurately locate and classify vehicles in traffic flows and working around total occlusions that hinder vehicle tracking. For real-time traffic monitoring, we present a traffic monitoring approach that overcomes the abovementioned challenges by employing convolutional neural networks that utilize You Only Look Once (YOLO). A real-time traffic monitoring system has been developed, and it has attracted significant attention from traffic management departments. Digitally processing and analyzing these videos in real time is crucial for extracting reliable data on traffic flow. Therefore, this study presents a real-time traffic monitoring system based on a virtual detection zone, Gaussian mixture model (GMM), and YOLO to increase the vehicle counting and classification efficiency. GMM and a virtual detection zone are used for vehicle counting, and YOLO is used to classify vehicles. Moreover, the distance and time traveled by a vehicle are used to estimate the speed of the vehicle. In this study, the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring data set (GRAM-RTM), and our collection data sets are used to verify the proposed method. Experimental results indicate that the proposed method with YOLOv4 achieved the highest classification accuracy of 98.91% and 99.5% in MAVD and GRAM-RTM data sets, respectively. Moreover, the proposed method with YOLOv4 also achieves the highest classification accuracy of 99.1%, 98.6%, and 98% in daytime, night time, and rainy day, respectively. In addition, the average absolute percentage error of vehicle speed estimation with the proposed method is about 7.6%.

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Determining vehicle speed based on video using convolutional neural network

Cited by 29 publications

References 18 publications

RETRACTED: Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

RETRACTED: Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

Modeling the Estimation Errors of Visual-based Systems Developed for Vehicle Speed Measurement

A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

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