Computer Vision Based Traffic Monitoring System for Multi-track Freeways

Iftikhar, Zubair; Dissanayake, P N; Vial, Peter James

doi:10.1007/978-3-319-09339-0_35

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Due to this, the object detection process is crucial in this work. An existing machine learning way, for example, grey image scaling, binarization of images, and background subtraction [2], [3] or sometimes edge detection [1], is required to complete this task. Without a doubt, this method has drawbacks and limitations such as the fact that when the vehicle's shadow appears in the image, the detection may not be completely accurate.…”

Section: Literature Surveymentioning

confidence: 99%

“…Various CNN architectures are employed in computer vision (OpenCV) to detect and classify objects. For example, "Faster R-CNN" [3], "Region-based Fully CNN" [4], "Regional based fully CNN (R-FCN)" [5], "YOLO" [6], "YOLOv2" [7]. Among many of these detection and classification techniques, you only look once the version 3 (YOLOv3) model is determined as the most suitable algorithm because of its high accuracy.…”

Section: Yolov3 Algorithmmentioning

confidence: 99%

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Vehicle Counting And Traffic Congestion Detection Using YOLOv3

Reddy¹

2023

IJFMR

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The purpose of this project is to utilize Python modules and a sophisticated deep learning algorithm to identify, categorize, track, and tally moving vehicles from highway CCTV footage. Additionally, the system predicts traffic congestion by analyzing the number of vehicles in consecutive video frames. When congestion is detected, the application automatically notifies the traffic police who receive a message on their mobile, prompting them to address the existing traffic jam in the area. The project's core involves a vision-based vehicle detection and counting system that relies on the YOLOv3 model and openCv-python library to achieve its objectives.

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Section: Literature Surveymentioning

confidence: 99%

Section: Yolov3 Algorithmmentioning

confidence: 99%

Vehicle Counting And Traffic Congestion Detection Using YOLOv3

Reddy¹

2023

IJFMR

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“…In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1][2][3][4][5][6][7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11][12][13]; vehicle classification [14][15][16][17][18][19][20][21][22][23][24][25][26]; vehicle counting system on streets and highways [27][28][29][30][31]; parking spot detection from the point of view of the car for parking assistants [32,33]; and parking spot monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

et al. 2021

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The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

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“…Traditional Machine Learning approach needs preprocessing approach to complete this task, e.g. image gray scaling, image binarization, and background subtraction [6], [7], [14], or sometimes using edge detection [5]. Of course, this approach has limitations, e.g.…”

Section: Introductionmentioning

confidence: 99%

A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model

Fachrie¹

2020

RESTI

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Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle counting system without having to track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pretrained model of YOLOv3 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a simple vehicle counting system to help human in classify and counting the vehicles that cross the street. The counting is based on four types of vehicle, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, our proposed system capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97.72%.

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Computer Vision Based Traffic Monitoring System for Multi-track Freeways

Cited by 8 publications

References 19 publications

Vehicle Counting And Traffic Congestion Detection Using YOLOv3

Vehicle Counting And Traffic Congestion Detection Using YOLOv3

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model

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