Intelligent Traffic Monitoring System Research Articles

Driver distraction and fatigue detection in images using ME‐YOLOv8 algorithm

AbstractDriving while inattentive or fatigued significantly contributes to traffic accidents and puts road users at a significantly higher risk of collision. The rise in road accidents due to driver inattention resulting from distractive objects, for example, mobile phones, drinking, or tiredness, requires intelligent traffic monitoring systems to promote road safety. However, outdated detection technologies cannot handle the poor accuracy and the lack of real‐time processing possibility especially when combined with the variations of driving environment. This paper introduces “ME‐YOLOv8” which operates driver`s distraction and fatigue through a modified version of YOLOv8, which includes modules multi‐head self‐attention (MHSA) and efficient channel attention (ECA) modules applied, where the goal of MHSA is to improve the sensitivity of global features and the ECA attentions focus on critical features. Additionally, a dataset was created containing 3660 images covering multiple distracted and drowsy driver scenarios. The results reflect the enhanced detection capabilities of ME‐YOLOv8 and demonstrate its effectiveness in real‐time scenarios. This study demonstrates a significant advancement in the application of AI to public safety and highlights the critical role that state‐of‐the‐art deep learning algorithms play in lowering the risks associated with distracted and tired driving.

A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles’ characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network’s ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements.

Development Status of Intelligent Traffic Management System

The intelligent traffic management system is the most important part of the intelligent traffic system. The research and analysis of the development status of the intelligent traffic management system can provide a research basis for its future development. This paper studies the three subsystems of the intelligent traffic management system: the intelligent traffic monitoring system, the By comparing the research methods used by scholars in different time periods for intelligent information service systems and signal control systems, it is found that with the application of technologies such as the Internet of Vehicles, big data, Internet of Things, and deep learning, the level of intelligent traffic management system management is constantly rising.

Docker Container-Based Framework of Apache Kafka Node Ecosystem: Vehicle Tracking System by License Plate Recognition on Surveillance Camera Feeds

Vehicle tracking and license plate recognition (LPR) over video surveillance cameras are essential intelligent traffic monitoring systems. Due to the enormous amount of data collected each day, it would be difficult to track vehicles by license plate in a real-world traffic setting. Large volumes of data processing, real-time request responses, and emergency scenario response may not be possible using conventional approaches. By combining license plate recognition with the docker container-based structure of the Apache Kafka node ecosystem, the suggested solution takes a novel approach to vehicle tracking. The primary components of our suggested framework for reading license plates are the identification of license plates and text data queries. License plate localization is performed with You Only Look Once version 3 (YOLOv3) and character recognition with Optical Character Recognition (OCR). The detected vehicle images with license plate results are published on related topics with Apache Kafka. Apache Kafka is a publish-subscribe (producers-consumers) messaging system and one of the most popular architectures used for streaming data. For each license plate search, a topic will be created in the framework where producers publish and consumers receive data. Thus, the workload of the operators will be reduced and they will be able to pay attention to more important events in traffic.

An Enhanced Detector for Vulnerable Road Users Using Infrastructure-Sensors-Enabled Device.

The precise and real-time detection of vulnerable road users (VRUs) using infrastructure-sensors-enabled devices is crucial for the advancement of intelligent traffic monitoring systems. To overcome the prevalent inefficiencies in VRU detection, this paper introduces an enhanced detector that utilizes a lightweight backbone network integrated with a parameterless attention mechanism. This integration significantly enhances the feature extraction capability for small targets within high-resolution images. Additionally, the design features a streamlined 'neck' and a dynamic detection head, both augmented with a pruning algorithm to reduce the model's parameter count and ensure a compact architecture. In collaboration with the specialized engineering dataset De_VRU, the model was deployed on the Hisilicon_Hi3516DV300 platform, specifically designed for infrastructure units. Rigorous ablation studies, employing YOLOv7-tiny as the baseline, confirm the detector's efficacy on the BDD100K and LLVIP datasets. The model not only achieved an improvement of over 12% in the mAP@50 metric but also realized a reduction in parameter count by more than 40%, and a 50% decrease in inference time. Visualization outcomes and a case study illustrate the detector's proficiency in conducting real-time detection with high-resolution imagery, underscoring its practical applicability.

Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences

Vehicle detection and classification are the most significant and challenging activities of an intelligent traffic monitoring system. Traditional methods are highly computationally expensive and also impose restrictions when the mode of data collection changes. This research proposes a new approach for vehicle detection and classification over aerial image sequences. The proposed model consists of five stages. All of the images are preprocessed in the first stage to reduce noise and raise the brightness level. The foreground items are then extracted from these images using segmentation. The segmented images are then passed onto the YOLOv8 algorithm to detect and locate vehicles in each image. The feature extraction phase is then applied to the detected vehicles. The extracted feature involves Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and KAZE features. For classification, we used the Deep Belief Network (DBN) classifier. Based on classification, the experimental results across the three datasets produced better outcomes; the proposed model attained an accuracy of 95.6% over Vehicle Detection in Aerial Imagery (VEDAI) and 94.6% over Vehicle Aerial Imagery from a Drone (VAID) dataset, respectively. To compare our model with the other standard techniques, we have also drawn a comparative analysis with the latest techniques in the research.

All‐Weather Self‐Powered Intelligent Traffic Monitoring System Based on a Conjunction of Self‐Healable Piezoresistive Sensors and Triboelectric Nanogenerators

AbstractA self‐powered and sustainable traffic monitoring system is highly required for future urban development. Herein, self‐healable piezoresistive sensors and triboelectric nanogenerators (TENGs) are constructed by in situ polymerization of polyvinyl alcohol‐polyacrylamide double network hydrogel in the presence of sodium alginate and tannic acid‐modified cellulose nanocrystals (denoted as PPC) for all‐weather self‐powered intelligent traffic monitoring applications. Because of hydrogen bonding and boron ester bonding, the resultant PPC‐based strain sensor can rapidly self‐heal and restore its sensing ability within 1 min with a self‐healing efficiency of 97.4%. Based on piezoresistive effect, the ions in sodium alginate endow the PPC‐based strain sensor with a relatively high gauge factor of 8.39, which can monitor the motion and fatigue of drivers. Based on triboelectrification effect, the PPC‐based TENG sensor can detect instantaneous vehicle speed, judge traffic accident liability, evaluate vehicle weight, and alert the driver to prevent accidents caused by drowsy driving. After partially replacing water in the PPC with glycerin, the resulting PPC‐based TENG sensor exhibits stable performance at temperatures ranging from ‐30 to 40 °C, ensuring its all‐weather monitoring ability. The all‐weather and self‐powered intelligent traffic monitoring system is promising for ensuring the security of future cities.

IoT based real-time traffic monitoring system using images sensors by sparse deep learning algorithm

Intelligent traffic monitoring systems are necessary and useful tools due to the emerging technologies related to the Internet of Things (IoT) and Artificial Intelligence (AI). The integration of both technologies can facilitate better urban traffic management for fast networks, such as 5G, 5G+ and 6G environments. However, existing studies focus on complex and expensive solutions or present a latency for generating new training models with accuracy not superior to 98%. Thus, this paper proposes a vehicle identification using a sparse and soft variant of the CNN-based approach, the LightSpaN, which offers a fast training model, without using a complex solution. The effectiveness of the proposed solution is evaluated using the Simulation of Urban MObility (SUMO) tool and a real vehicle traffic implementation using IoT devices. The proposal identified the majority kind of vehicles in a short period of time, faster and more accurately than the related works. The results validated the proposed solution for a real-time traffic monitoring system, presenting an average accuracy of around 99.9% for emergency vehicles. Furthermore, a reduction of both total waiting time and total traveling time was reached by our proposal.

Intelligent Based Real Time Traffic Monitoring in Smart Cities

PC-based Intelligent Traffic Monitoring System is unable to deal with the on-going issues surrounding congestion. The current traffic light models are not suited to tackle problems such as traffic jams, ease of access for emergency vehicles and prevention of accidents. In order to counteract these issues, we propose ‘PC-based Intelligent Traffic Monitoring System with Real-time Analysis for Smart Cities’. The system consists of a network of cameras installed at strategic locations across the city, which capture images of vehicles and pedestrians. These images are then processed by intelligent algorithms, which analyse the data and provide real-time traffic information. The system is also equipped with a real-time monitoring interface, which displays the traffic data in an easy-to-understand format. The system is designed to help cities optimize their traffic flow and reduce congestion. By analysing real-time traffic data, city officials can make informed decisions about traffic management and can take immediate action to alleviate congestion. This project will focus on two aspects of implementation. First and foremost, to make traffic light controlling more efficient, image processing alongside with embedded system, will be used. This system will intelligently decide when to alternate signals based on the emergency vehicle on each lane which will increase road capacity and traffic flow. Secondly the system favours school students by giving green signal to give them free passage. It is a matter of concern, as emergency vehicles are being categorized as priority vehicles, so their waiting time should be minimum.

Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Traffic speed forecasting plays an important role in intelligent traffic monitoring systems. Existing methods mostly predefine a fixed adjacency matrix to capture the spatial correlation between sensors in a traffic network. However, there are multiple hidden spatial correlations between sensors. A single fixed adjacency matrix cannot adaptively capture multiple spatial correlations. To overcome this limitation, we proposed a novel multiadaptive spatiotemporal flow graph neural network (MAF-GNN) for traffic speed forecasting. Specifically, MAF-GNN mainly consists of a multiadaptive adjacency matrix mechanism and a spatiotemporal flow mechanism. The multiadaptive adjacency matrix mechanism was proposed to adaptively capture multiple hidden spatial correlations between sensors. The spatiotemporal flow mechanism was proposed to further enhance the capture of temporal and spatial correlations. The experimental results on two real-world traffic datasets, METR-LA and PeMS-Bay, demonstrated the superiority of MAF-GNN. MAF-GNN outperformed baseline models in 1-h ahead forecasting.

Intelligent traffic monitoring and analysis system to detect attacks in software-defined networks

Today, the task of monitoring, security, management, traffic prioritization and optimization of the overall load on the network infrastructure is becoming extremely important not only for the corporate sector, but also for mobile and fixed-line operators. Current trends in Internet content services indicate that the requirements for traffic management are unpredictable. Therefore, service providers must implement new algorithms to monitor and analyze network traffic to detect attacks in today's networks, where software can be updated to support changing traffic management and control requirements as needed. That is why this paper developed a unique algorithm for monitoring and analyzing network traffic, which improved the efficiency of standard intrusion detection and prevention systems by using intelligent multifractal processes for analyzing incoming traffic. It has been experimentally proved that the use of the developed algorithms in the communication infrastructure allowed to reduce data losses up to 5 % if malicious traffic was present in the communication channels.

A hierarchical RCNN for vehicle and vehicle license plate detection and recognition

<span>Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.</span>

Video Deraining Using the Visual Properties of Rain Streaks

In computer vision applications, the visibility of the video content is crucial to perform analysis for better accuracy. The visibility can be affected by several atmospheric interferences in challenging weather &#x2013; one such interference is the appearance of rain streaks. Recently, rain streak removal has achieved plenty of interest among researchers, as it has some exciting applications such as autonomous cars, intelligent traffic monitoring systems, multimedia, etc. In this paper, we propose a novel and simple method of rain streak removal by combining three novel extracted visual features focusing on the temporal appearance, wide shape and relative location of the rain streak. We called it the TAWL (<i>Temporal Appearance, Width, and Location</i>) method. The proposed TAWL method adaptively uses features from different resolutions and frame rates. Moreover, it progressively processes features from the upcoming frames so that it can remove rain in real-time. Experiments have been conducted using video sequences with both real rain and synthetic rain to compare the performance of the proposed method against the relevant state-of-the-art methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by removing more rain streaks while keeping other moving regions.

The Moral Decision-Making Capacity of Autonomous Mobility Technologies: Route Planning Algorithms, Simulation Modeling Tools, and Intelligent Traffic Monitoring Systems

The Moral Decision-Making Capacity of Autonomous Mobility Technologies: Route Planning Algorithms, Simulation Modeling Tools, and Intelligent Traffic Monitoring Systems

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. Consequently, many traffic-related problems have emerged, such as traffic jams and excessive numbers and types of vehicles. To solve traffic problems, road data collection is important. Therefore, in this paper, we develop an intelligent traffic-monitoring system based on you only look once (YOLO) and a convolutional fuzzy neural network (CFNN), which record traffic volume, and vehicle type information from the road. In this system, YOLO is first used to detect vehicles and is combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector-CFNN) and a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed method achieved an accuracy of 90.45% on the Beijing Institute of Technology public dataset. On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods. On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting. In addition, the proposed system can maintain a detection speed of more than 30 frames per second in the AGX embedded platform. Therefore, the proposed intelligent traffic monitoring system is suitable for real-time vehicle classification and counting in the actual environment.

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

In implementing the Intelligent Traffic Monitoring System (ITMS), timely and effective access to road traffic information is an essential link. It requires an effective traffic Information Acquisition System (IAS) to collect real-time data and transmit the collected information to the background for processing. Therefore, this paper studies on-road vehicle information recognition based on Deep Learning (DL). Firstly, a framework of traffic IAS is proposed. Then, an improved MT-GooGleNet model based on Convolutional Neural Network (CNN) is proposed to locate and recognize vehicles in traffic images. Finally, the performance of the model is analyzed by simulation. The experimental results of vehicle position recognition show that the classification accuracy of Multi-Task (MT)-GooGleNet after fine-tuning is 99.5%. Compared with other models, the MT-GooGleNet model proposed is the best in vehicle position recognition, and its positioning accuracy is very high. The results of vehicle identification show that after data enhancement and pre-training, the testing set accuracy of the MT-GooGleNet model is 79.96%. The results show that the model&#x2019;s accuracy has been dramatically improved after processing. The research provides a reference for establishing IAS in the future.

Fully self-powered instantaneous wireless traffic monitoring system based on triboelectric nanogenerator and magnetic resonance coupling

The self-powered wireless sensor systems are highly sought for the information era, as they are able to link everything together, yet without the need for electric power supply. They have been exploited for applications in various fields, particularly for environment-related ones. Here, we propose a fully self-powered triboelectric nanogenerator (TENG)-based wireless traffic monitoring system for real-time monitoring of illegally entered electric motorbikes on the footpaths, and flow rates of pedestrians and motorbikes, and speed and driving direction of electric motorbikes on non-motorized lanes. The TENG was used to directly convert the energy from pedestrian’s stepping and electric motorbike’s rolling into an oscillating signal with encoded sensing information, and transmit it to the receiver wirelessly via magnetic resonance coupling. Results showed that the multi-variable traffic monitoring system can identify pedestrians and motorbikes, check the illegally entered motorbikes on the footpath, and measure speed of the motorbikes with an accuracy of over 94%, monitor driving direction of electric motorbikes on non-motorized lanes. The self-powered wireless traffic monitoring system is simple in structure, low cost, easy to install, and spontaneous response to the traffic condition, and does not contain electronic devices, microcontrollers and transmission chips but the passive components, hence it has great application potential as a green intelligent traffic monitoring system. The self-powered wireless traffic monitoring system based on TENG, which can monitor real-time electric motorbikes illegally entering in the footpaths, and flow rates of pedestrians and motorbikes, and speed measurement, driving direction of electric motorbikes on non-motorized lanes. • A triboelectric nanogenerator (TENG) based self-powered instantaneous wireless traffic monitoring system is developed which yet does not contain electronic devices, but the passive components. • The traffic recognition monitoring system can identify pedestrians on the sidewalk and illegal vehicles entering the footpath. • The traffic speed monitoring system can realize the function of electric vehicle speed measurement and driving direction judgment.

Machine learning-based human-robot interaction in ITS

In the last few years, intelligent transport systems (ITS) have drawn growing attention, and these applications would have a clear and more comfortable experience for transportation. ITS provides applications with a chance to address the future condition on the route beforehand. The major issues in ITS to accomplish a precise and effective traffic flow prediction system are essential. Therefore, in this paper, a machine learning-assisted intelligent traffic monitoring system (ML-ITMS) has proposed improving transportation protection and reliability to tackle several challenges. The suggested ML-ITMS uses mathematical models to improve the accuracy estimation of traffic flow and nonparametric processes. The Machine Learning-based (ML) method is one of the best-known methods of nonparametric. It requires less prior information about connections between various traffic patterns, minor estimation limitations, and better suitability of nonlinear traffic data features. Human-Robot Interaction (HRI) helps resolve crucial issues concurrently on both the customers and service supplier levels at both ends of the transport system. Thus the experimental results show the proposed ML-ITMS to enhance traffic monitoring to 98.6% and better traffic flow prediction systems than other existing methods.

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.

Research on Intelligent Traffic Monitoring System Based on Image Recognition Technology

With the rapid development of China’s national economy, the number of cars on domestic expressways and in cities has greatly increased. Cities have more and more facilities, such as roads and parking lots, while traffic control and safety management requirements are increasing. In Japan and overseas, Intelligent Transportation System (ITS) has become the main direction of current traffic management development and image recognition based on image recognition technology. As one of the core technologies of computer intelligent transportation system, computer technology is an important part of intelligent transportation system. The wide application of this technology will make a great contribution to the automation of traffic management in China.