Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework

In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.

Violation of Traffic Signaling Equipment by Road Users (A Research in the Ternate City Police Legal Area in Ternate City)

This study aims (1) to determine the application of criminal sanctions to violations of traffic signaling devices (TST) based on Law No. 22 of 2009 in the City of Ternate. (2) what are the factors that influence the imposition of sanctions for violations of traffic signaling devices as seen from Law Number 22 of 2009. This research was conducted in the jurisdiction of the City of Ternate. The type of research used is empirical, namely research using an approach model by looking at the legal reality in society. In this study, the authors conducted interviews with the Satlantas Polres Kota Ternate and also made direct observations in the field and also distributed questionnaires. This study uses qualitative and quantitative analysis techniques. The results showed that the application of sanctions for violations of TST in Ternate City in accordance with Law No. 22 of 2009 concerning Road Traffic and Transportation has not been effectively implemented in Ternate City because every year there is an increasing number of TST violations. In the application of sanctions for traffic violations, a mature concept is needed and can be used properly so that the implementation of sanctions carried out can run in accordance with the applicable law, the concepts used are traffic management, traffic control activities and traffic control activities. presumably can help in the application of sanctions for violators so that it can create comfort in traffic. In the application of APIIL criminal sanctions in Ternate City, there are factors that influence the application of sanctions against TST violations in Ternate City, namely, law enforcement factors, community legal awareness factors are still weak to comply with TST, facilities and facilities factors and disciplined cultural factors from motorized vehicle drivers. which is still very lacking.

Costs and Consequences of Traffic Fines and Fees: A Case Study of Open Warrants in Las Vegas, Nevada

Traffic stops and tickets often have far-reaching consequences for poor and marginalized communities, yet resulting fines and fees increasingly fund local court systems. This paper critically explores who bears the brunt of traffic fines and fees in Nevada, historically one of the fastest growing and increasingly diverse states in the nation, and one of thirteen US states to prosecute minor traffic violations as criminal misdemeanors rather than civil infractions. Drawing on legislative histories, we find that state legislators in Nevada increased fines and fees to raise revenues. Using descriptive statistics to analyze the 2012–2020 open arrest warrants extracted from the Las Vegas Municipal Court, we find that 58.6% of all open warrants are from failure to pay tickets owing to administrative-related offenses—vehicle registration and maintenance, no license or plates, or no insurance. Those issued warrants for failure to pay are disproportionately for people who are Black and from the poorest areas in the region. Ultimately, the Nevada system of monetary traffic sanctions criminalizes poverty and reinforces racial disparities.

A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition model, this study adopted multitask learning in the license plate detection stage, used the convolutional neural networks of single-stage detection, RetinaFace, and MobileNet, as approaches to license plate location, and completed the license plate sampling through the calculation of license plate skew correction. In the license plate character recognition stage, the Convolutional Recurrent Neural Network (CRNN) integrated with the loss function of the CTC model was employed as a segmentation-free and highly robust method of license plate character recognition. In this study, after the license plate recognition model, DLPR, trained the PVLP dataset of vehicle images provided by company A in Taiwan’s data processing industry, it performed tests on the PVLP dataset, indicating that its precision was 98.60%, recognition accuracy was 97.56%, and recognition speed was FPS > 21. In addition, according to the tests on the public AOLP dataset of Taiwan’s vehicles, its recognition accuracy was 97.70% and recognition speed was FPS > 62. Therefore, not only can the DLPR model be applied to the license plate recognition of real-time image streams in the future, but also it can assist the data processing industry in enhancing the accuracy of license plate recognition in photos of traffic violations and the performance of traffic service operations.

Evaluation of a Public Technology-Based Traffic Enforcement Program

While police enforcement is a well-known means of reducing traffic violations, it is also recognized that other agents should be involved in creating sustainable deterrence. This paper describes and evaluates the Israeli Road Guards program, a new and unique type of traffic enforcement, which enables simple technology-based enforcement of traffic violations by citizens. In its 24 months of operation, more than 3400 volunteers who submitted over 64,000 violation reports were involved in this program. Each report went through a rigorous evaluation process. More than 80% of the submitted reports were rejected in the various stages of the procedure. In 13.7% of the cases a notice letter was sent, and in 4.3% of cases (reflecting the most severe offenses) a citation was issued by the police. The monthly rate of report submission by the volunteers was at its highest initially, then decreased and stabilized after about six months at 1.4 reports per month. The proportion of active volunteers also decreased over time to a level of 0.26 at the end of the study period. The violation types reported within the program differed substantially from those captured by police enforcement. These differences are likely due to the manner in which each mode of enforcement was performed. The most common violations reported by volunteers were lane deviations, red light running and driving on the roads’ shoulders, which are easily documented by means of video recordings. They are also associated with higher crash risks. Thus, the results show that such public technology-based traffic enforcement, which can be carried out during regular daily driving and does not require anyone to make extra trips, may efficiently complement traditional police enforcement.

A Live-Video Automatic Number Plate Recognition (ANPR) System Using Convolutional Neural Network (CNN) with Data Labelling on an Android Smartphone

Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicles, tracking traffic violations, and parking management system. This paper discusses a live-video ANPR system using CNN developed on an Android smartphone embedded with a camera with limited resolution and limited processing power based on Malaysian license plate standards. In terms of system performance, in an ideal outdoor environment with good lighting and direct or slightly skewed camera angle, the recognition works perfectly with a computational time of 0.635 seconds. However, this performance is affected by poor lighting, extremely skewed angle of license plates, and fast vehicle movement.