Vehicle Detection and Counting to Identify Traffic Density in The Intersection of Road Using Image Processing

Lahinta, Fitria; Zainuddin, Zainal Abidin; Syarif, Syafruddin

doi:10.4108/eai.2-5-2019.2284706

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Its intensity distinguishes each pixel in RGB (Red, Green, Blue). Then each pixel is calculated as a foreground or background value using the following equation (Lahinta et al, 2019):…”

Section: • Number Of Vehiclesmentioning

confidence: 99%

Indicator of Highway Conditions on Traffic Density Levels

Rosita

Akbar²,

Kurniawan³

2023

ijeeit

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Traffic problems, especially congestion which is the impact of high levels of traffic density, are problems that have not been properly resolved, especially in big cities in Indonesia. In this article, 7 features of road conditions are proposed on the level of traffic density. These features include the number of vehicles, the average speed of the vehicle, the area of the main road, the length of traffic that has been determined, the duration of traffic congestion, and the time of traffic congestion. Determining the level of traffic density can be determined by utilizing the MCDM hybrid technique to produce information that can be used to support decision-making in traffic management or other policies such as formulating policies on the buying and selling of private vehicles. The number of indicators related to computer vision for presenting information on the level of traffic density requires a large amount of time and memory. Besides that, the perception of a decision maker in determining priority weights also influences the results of the information on the level of traffic density.

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“…Its intensity distinguishes each pixel in RGB (Red, Green, Blue). Then each pixel is calculated as a foreground or background value using the following equation (Lahinta et al, 2019):…”

Section: • Number Of Vehiclesmentioning

confidence: 99%

Indicator of Highway Conditions on Traffic Density Levels

Rosita

Akbar²,

Kurniawan³

2023

ijeeit

View full text Add to dashboard Cite

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“…A method for counting automobiles and categorizing their density level was presented by [Lahinta et al (2019)]. The video data for this investigation was gathered in Manado, Sulawesi.…”

Section: Related Workmentioning

confidence: 99%

Traffic Monitoring System for Smart City Based on Traffic Density Estimation

Bidwe¹,

Kale²,

Bidwe³

2022

INDJCSE

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Nowadays, traffic monitoring systems are at the frontline of smart city movement, and traffic density estimation is useful to a traffic monitoring system. The system of this work estimates traffic density using a five-layered CNN with a variety of input feature maps and filter sizes. There are 64, 64, 96, 96, and 96 feature maps for each pair of convolutions and max-pooling layers, and each pair's corresponding filter sizes are 5*5, 3*3, 5*5, 3*3, and 3*3. The proposed system divides the traffic into three categories: High, Medium, and Low traffic based on images that are taken from traffic videos that are recorded by traffic surveillance cameras. To test the system, we used the WSDT (Washington State Department of Traffic Transportation) Dataset of recorded video footage from the highway CCTVs in cities Seattle and Washington. The model is evaluated using parameters such as 0.99 precision, 0.99 recall, 0.99 f1-score, and model accuracy of 99.6 %. By examining the dataset, we have trained the model in such a manner to produce better results.

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