Real-Time Traffic Rules Infringing Determination Over the Video Stream: Wrong Way and Clearway Violation Detection

Şentaş, Ali; Kul, Seda; Sayar, Ahmet

doi:10.1109/idap.2019.8875889

Cited by 13 publications

(3 citation statements)

References 20 publications

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“…The research focused on developing Gaussians based model, MeadianFlow algorithm for detection of one-way violation and speed violation detection. It was planned to develop a publish/subscribe distributed system model in which users can track infringements only in the type that they only want [5]. Hough transforms and bus cameras were used to monitor the road congestion information and detect violation of vehicles in order to achieve early warning and real-time monitoring.…”

Section: Literature Reviewmentioning

confidence: 99%

Untitled

2023

IJITCS

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A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to detect traffic violations in real-time using edge computing, which reduces the time to detect. Different machine learning models and algorithms were applied to detect traffic violations like traveling without a helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson Nano, as the fps is quite low.

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Section: Literature Reviewmentioning

confidence: 99%

Untitled

2023

IJITCS

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“…Similar work is reported in [19] where IP (Internet Protocol)-enabled cameras are used to monitor the roads to monitor traffic violations. Another work that uses real-time video feedback for traffic monitoring is reported in [20]. In a similar approach, works in [13,21,22] explore the use of Unmanned Aerial Vehicle (UAV) for road safety, traffic, and highway infrastructure management.…”

Section: Existing Speed-limit Traffic Violation Recording Systemsmentioning

confidence: 99%

Autonomous Real-Time Speed-Limit Violation Detection and Reporting Systems Based on the Internet of Vehicles (IoV)

Mohamed

Parvez

Al-Shalfan

et al. 2021

Journal of Advanced Transportation

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Over-/underspeeding is one of the leading causes of road accidents. Traditional systems of detecting and reporting speed-limit violations are not suitable for smart cities. Even the sophisticated conventional systems that use cameras or RFIDs for automating speed-limit violations have several drawbacks, including cost, complexity, reliability, and maintenance. In this paper, we present two systems based on the Internet of Vehicles (IoV) to automatically detect speed-limit violations and autonomously report the committed violations to the authorities. Our systems require no extra hardware or equipment: only the On-Board Unit (OBU), the Road Side Unit (RSU), and the Cloud Server software have to be updated to have a fully functioning system as long as the IoV infrastructure is deployed. One of the systems will be installed on the OBU. A second alternative system design is to use Cloud Servers (CSs) and the IoV beacons that are sent from the vehicles. Additionally, unlike the existing systems installed in specific locations, all roads in the smart cities and highways will be fully monitored. Adaptive fine calculation according to new dynamic policies can be easily integrated into the proposed system. Furthermore, the proposed system can accurately operate in all weather conditions. Moreover, it allows the dynamic adjustment of the speed limits according to the current weather conditions. We have validated the proposed system by building a prototype system that effectively and accurately detects and reports over-/underspeed traffic violations alongside any road.

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“…It has been observed that studies on deep learning-based such as CNN give successful results in many different areas in object detection and tracking. [4][5][6][7][8][9][10][11] Similarly, Im2GPS 12,13 uses an artificial neural network to obtain information from photographs. It adopts the "lazy learning" approach inspired by the Support Vector Machine -K-Nearest Neighbors (SVM-KNN) 14 and pre-audited KNN developments ("local" learning methods 15 ) as artificial neural network models.…”

Section: Related Workmentioning

confidence: 99%

Image‐based locating and guiding for unmanned aerial vehicles using scale invariant feature transform, speeded‐up robust features, and oriented fast and rotated brief algorithms

Bal

Erdem

Kul

et al. 2021

Concurrency and Computation

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This study provides a method for determining the location information from a photograph taken by an Unmanned Aerial Vehicle (UAV). In today's defense industry, the goal is to design a reliable navigation system that can operate even when the Internet and GPS systems are unavailable. In the proposed system, it is expected that there is an image database in which each image has its own metadata in JavaScript Object Notation format that defines the geolocation information for the corresponding image. In real-time, UAV flies captures an image and uses a matching algorithm to compare it with each and every photo in the database. The image with the highest match rate in the database and its metadata is returned. The UAV extracts its own location information from the metadata. For image matching, Scale Invariant Feature Transform, Speeded-Up Robust Features, and Oriented FAST and Rotated BRIEF algorithms are used. The proposed system is tested and evaluated by using a real-world dataset obtained from Flickr's city images. The proposed approach is proved to work with a success rate of 97.14%.

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Real-Time Traffic Rules Infringing Determination Over the Video Stream: Wrong Way and Clearway Violation Detection

Cited by 13 publications

References 20 publications

Untitled

Untitled

Autonomous Real-Time Speed-Limit Violation Detection and Reporting Systems Based on the Internet of Vehicles (IoV)

Image‐based locating and guiding for unmanned aerial vehicles using scale invariant feature transform, speeded‐up robust features, and oriented fast and rotated brief algorithms

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