Police‐less multi‐party traffic violation detection and reporting system with privacy preservation

Ozkul, Mukremin; Capuni, Ilir

doi:10.1049/iet-its.2017.0122

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…But we have noticed that illegal parking of rental vehicles is also increasing. However, taxis have the characteristics of mobility, dispersion, and nonspecific service groups [2][3][4]. So relevant managers urgently need a fast and accurate method of judging taxi violations to facilitate the management of urban traffic and support the green and efficient demand for intelligent transportation.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Yang

2021

Scientific Programming

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Taxi has the characteristics of strong mobility and wide dispersion, which makes it difficult for relevant law enforcement officers to make accurate judgment on their illegal acts quickly and accurately. With the investment of intelligent transportation system, image analysis technology has become a new method to determine the illegal behavior of taxis, but the current image analysis method is still difficult to support the detection of illegal behavior of taxis in the actual complex image scene. To solve this problem, this study proposed a method of taxi violation recognition based on semantic segmentation of PSPNet and improved YOLOv3. (1) Based on YOLOv3, the proposed method introduces spatial pyramid pooling (SPP) for taxi recognition, which can convert vehicle feature images with different resolutions into feature vectors with the same dimension as the full connection layer and solve the problem of repeated extraction of YOLOv3 vehicle image features. (2) This method can recognize two different violations of taxi (blocking license plate and illegal parking) rather than only one. (3) Based on PSPNet semantic segmentation network, a taxi illegal parking detection method is proposed. This method can collect the global information of road condition images and aggregate the image information of different regions, so as to improve the ability to obtain the global information orderly and improve the accuracy of taxi illegal parking detection. The experimental results show that the proposed method has excellent recognition performance for the detection rate of license plate occlusion behavior DR is 85.3%, and the detection rate of taxi illegal parking phenomenon DR is 96.1%.

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Section: Introductionmentioning

confidence: 99%

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Yang

2021

Scientific Programming

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“…However, although highly capable, these systems require costly infrastructure that is difficult for low-and middle-income countries to deploy extensively. In more recent years, technology has moved toward more cost-effective distributed systems that rely on embedded systems installed locally on vehicles and vehicular ad-hoc networks (VANETs) [29][30][31][32][33]. These systems are also difficult to apply in low-and middle-income countries because it is nearly impossible to force every road user to equip their vehicles with the needed embedded systems.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Highly Scalable, Low-Cost Distributed Traffic Violation Enforcement System in Phuket, Thailand

et al. 2021

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The number of global road traffic accidents is rising every year and remains undesirably high. One of the main reasons for this trend is that, in many countries, road users violate road safety regulations and traffic laws. Despite improvements in road safety legislation, enforcement is still a major challenge in low- and middle-income countries. Information technology solutions have emerged for automated traffic enforcement systems in the last decade. They have been tested on a small scale, but until now, the cost of deployment of these systems is generally too high for nation-wide adoption in low- and middle-income countries that need them the most. We present the architectural design of a traffic violation enforcement system that can optimize the cost of deployment and resource utilization. Based on the proposed architecture, we describe the implementation and deployment of the system, and perform a comparison of two different versions of the video-based enforcement system, one using classical computer vision methods and another using deep learning techniques. Finally, we analyze the impact of the system deployed in Phuket, Thailand from 2017 to the present in terms of local road users’ compliance and the road safety situation. We conclude that the system has had a positive impact on road safety in Phuket at a moderate cost.

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Intelligent Vehicle Violation Detection System Under Human–Computer Interaction and Computer Vision

Ren

2024

Int J Comput Intell Syst

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In view of the current problems of low detection accuracy, poor stability and slow detection speed of intelligent vehicle violation detection systems, this article will use human–computer interaction and computer vision technology to solve the existing problems. First, the picture data required for the experiment is collected through the Bit Vehicle model dataset, and computer vision technology is used for preprocessing. Then, use Kalman filtering to track and study the vehicle to help better predict the trajectory of the vehicle in the area that needs to be detected; finally, use human–computer interaction technology to build the interactive interface of the system and improve the operability of the system. The violation detection system based on computer vision technology has an accuracy of more than 96.86% for the detection of the eight types of violations extracted, and the average detection is 98%. Through computer vision technology, the system can accurately detect and identify vehicle violations in real time, effectively improving the efficiency and safety of traffic management. In addition, the system also pays special attention to the design of human–computer interaction, provides an intuitive and easy-to-use user interface, and enables traffic managers to easily monitor and manage traffic conditions. This innovative intelligent vehicle violation detection system is expected to help the development of traffic management technology in the future.

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Police‐less multi‐party traffic violation detection and reporting system with privacy preservation

Cited by 8 publications

References 15 publications

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Design and Implementation of a Highly Scalable, Low-Cost Distributed Traffic Violation Enforcement System in Phuket, Thailand

Intelligent Vehicle Violation Detection System Under Human–Computer Interaction and Computer Vision

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