Vehicle Speed Monitoring using Convolutional Neural Networks

Barth, Vitor Bruno de Oliveira; Oliveira, Ruy de; Oliveira, Mário Anderson de; Nascimento, Valtemir Emerencio do

doi:10.1109/tla.2019.8896823

Cited by 14 publications

(5 citation statements)

References 29 publications

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“…This technique recognizes vehicle's features and the marking of points that will be followed, to promote correspondence between different frames, and then connects the positions of the same points of the vehicle in the frame sequence. The choice of points (features) to be followed is essential for tracking accuracy [17]. However, object tracking algorithms allow tracking of the path taken by an object in a set of video www.ijacsa.thesai.org frames.…”

Section: B Vehicle Trackingmentioning

confidence: 99%

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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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Section: B Vehicle Trackingmentioning

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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“…Классические методы слежения за обнаружением полагаются исключительно на подсказки движения от детектора и решают проблему ассоциации данных с помощью методов оптимизации [15]. Хорошо известными примерами являются отслеживание множественных гипотез [16] и объединенный фильтр ассоциации вероятностных данных [17]. Эти методы решают проблему ассоциации на покадровой основе, но их комбинаторная сложность экспоненциально зависит от количества отслеживаемых объектов, что делает их непригодными для отслеживания в реальном времени.…”

Section: многообъектное отслеживаниеunclassified

Predicting and Managing Lane Capacity at a Signal-Controlled Intersection

Shepelev¹,

Gorodokin²,

Slobodin³

et al. 2021

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“…Several existing solutions are based on the use of traffic cameras located directly above the carriageway or on the side of it [26,27]. In [28], the authors manually marked the measurement zone in the camera image. It is a rectangular area perpendicular to the traffic flow.…”

Section: Speed Detectionmentioning

confidence: 99%

Real-time monitoring of traffic parameters

et al. 2020

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This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

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Vehicle Speed Monitoring using Convolutional Neural Networks

Cited by 14 publications

References 29 publications

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

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

Predicting and Managing Lane Capacity at a Signal-Controlled Intersection

Real-time monitoring of traffic parameters

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