Smart traffic control system with application of image processing techniques

Hasan, Md. Munir; Saha, Gobinda; Hoque, Abu Sayed Md. Latiful; Majumder, Md. Badruddoja

doi:10.1109/iciev.2014.6850751

Cited by 41 publications

(6 citation statements)

References 8 publications

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“…Notwithstanding considerable improvements in image processing techniques, available commercial software are dogged by problems of inability to detect and handle vehicle occlusions from camera view [28]- [30], limited functionality in severe weather conditions [31], [32], undesired factors like damaged road or white marks on the road surface and shadows of trees and buildings and nighttime vehicle detection [33], and overcrowded roads [34]. Another major drawback of video analytic algorithms is the lack of inter-system compatibility with already installed hardware, unless these two components are products of the same manufacturer.…”

Section: Developments In Image Processing Methodsmentioning

confidence: 99%

Image Processing in Intelligent Traffic Management

2019

ijrte

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Traffic monitoring and traffic control have always been challenging tasks. Intelligent Transportation Systems (ITS) based on wide range of technologies have certain practical challenges in their application and implementation. Video surveillance has proven advantageous over traditional systems based on inductive loops sensors and detectors for traffic monitoring. Accurate traffic density estimation which is basic to tackling traffic congestions requires detection of vehicles, assessing their speed, and tracking vehicles passing through surveillance zones. Image processing techniques require processing of large number of image frames for real-time applications in traffic management. More efficient and less costly image processing techniques for accurate vehicle detection and density determination are required for developing more effective traffic management systems. There is a need for developing algorithms with robust performance under heavy traffic loads and varied environmental conditions. Developments in artificial intelligence offer new vistas in image processing for regulation and management of traffic by signal control mechanisms and creation of neural networks for unhindered traffic flow.

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Section: Developments In Image Processing Methodsmentioning

confidence: 99%

Image Processing in Intelligent Traffic Management

2019

ijrte

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“…Md. Munir Hasan et al [3], Gobinda Saha, Aminul Hoque, and Md. Badruddoja Majumder, presented a method for determining traffic density on various traffic points.…”

Section: Figure 2 Block Diagram Of Analysis and Monitoring Of A Higmentioning

confidence: 99%

Traffic Density Analysis using Image Processing

Ashwini¹,

Jaya²,

Samiksha³

et al. 2018

IJCA

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Real time traffic density prediction and analysis have recently gained popularity as compared to traditional traffic density system using CCTVs. The popularity and need of traffic monitoring at public places, industrial sector, and residential areas have supported the widespread use of real time traffic monitoring. The motion of the vehicle is one of the basic parameter for identification of the flow of traffic on roads. The traffic flow on the roads can be basically categorized into heavy, medium and low traffic. Majorly thresholds that are used to correctly classify the traffic in any frame. Background subtraction, edge detection, optical flow estimation, BLOB( Binary Large Object) detection, magnetic loops, computer vision filtration techniques, closure operation are some techniques that are combined by various researchers and used to correctly classify the nature of vehicular traffic in a frame. However, the vehicular movement"s nature is dynamic and unpredictable. For traditional techniques that are been used over years have a few challenges including the color of the road and obstacles such as shadow and illumination. The colors of majority of vehicles observed on roads are white, silver, and black. The roads also are cement based or tar-based those make them an obstacle in traditional systems. This paper presents a new blend of various studied techniques for Traffic Density Analysis.

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“…For example, the ITS provides multiple services such as transport and traffic monitoring. The aim of this monitoring is to acquire and analyze vehicle movements, provide accurate data, and important statistics about the type and shape of the vehicle, as well as the evaluation of road traffic and safety [1], [3]. Thus, transport and traffic monitoring comes to facilitate the human labor, by vision-based tasks that a computer or automated system can perform [4].…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring

Kherraki

Ouazzani

2022

IJ-AI

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Nowadays, intelligent transportation system (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to traffic monitoring. The classification of emergency vehicles in traffic surveillance cameras provides an early warning to ensure a rapid reaction in emergency events. Computer vision technology, including deep learning, has many advantages for traffic monitoring. For instance, convolutional neural network (CNN) has given very good results and optimal performance in computer vision tasks, such as the classification of vehicles according to their types, and brands. In this paper, we will classify emergency vehicles from the output of a closed-circuit television (CCTV) camera. Among the advantages of this research paper is providing detailed information on the emergency vehicle classification topic. Emergency vehicles have the highest priority on the road and finding the best emergency vehicle classification model in realtime will undoubtedly save lives. Thus, we have used eight CNN architectures and compared their performances on the Analytics Vidhya Emergency Vehicle dataset. The experiments show that the utilization of DenseNet121 gives excellent classification results which makes it the most suitable architecture for this research topic, besides, DenseNet121 does not require a high memory size which makes it appropriate for real-time applications.<p> </p>

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Smart traffic control system with application of image processing techniques

Cited by 41 publications

References 8 publications

Image Processing in Intelligent Traffic Management

Image Processing in Intelligent Traffic Management

Traffic Density Analysis using Image Processing

Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring

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