scholarly journals Wireless Magnetic Sensor Network for Road Traffic Monitoring and Vehicle Classification

2016 ◽  
Vol 17 (4) ◽  
pp. 274-288 ◽  
Author(s):  
Vladan Velisavljevic ◽  
Eduardo Cano ◽  
Vladimir Dyo ◽  
Ben Allen
Keyword(s):  
Road Traffic ◽  
Low Cost ◽  
Traffic Monitoring ◽  
Vital Role ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
High Rate ◽  
Vehicle Classification ◽  
Accurate Estimation ◽  
Vehicle Speed

Abstract Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification.

scholarly journals Traffic Measurement and Vehicle Classification with Single Magnetic Sensor

2005 ◽  
Vol 1917 (1) ◽  
pp. 173-181 ◽  
Author(s):  
Sing Yiu Cheung ◽  
Sinem Coleri ◽  
Baris Dundar ◽  
Sumitra Ganesh ◽  
Chin-Woo Tan ◽  
...  
Keyword(s):  
Field Experiments ◽  
Low Cost ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
Sensor Data ◽  
Vehicle Classification ◽  
Traffic Measurement ◽  
Yield Information ◽  
Detection Capabilities ◽  
Better Than

Wireless magnetic sensor networks offer an attractive, low-cost alternative to inductive loops for traffic measurement in freeways and at intersections. In addition to providing vehicle count, occupancy, and speed, these sensors yield information (such as non-axle-based vehicle classification) that cannot be obtained from standard loop data. Because such networks can be deployed quickly, they can be used (and reused) for temporary traffic measurement. This paper reports the detection capabilities of magnetic sensors on the basis of two field experiments. The first experiment collected a 2-h trace of measurements on Hearst Avenue in Berkeley, California. The vehicle detection rate was better than 99% (100% for vehicles other than motorcycles), and estimates of average vehicle length and speed appear to have been better than 90%. The measurements also yield intervehicle spacing or headways, revealing interesting phenomena such as platoon formation downstream of a traffic signal. Results of the second experiment are preliminary. Sensor data from 37 passing vehicles at the same site are processed and classified into six types. Sixty percent of the vehicles are classified correctly when length is not used as a feature. The classification algorithm can be implemented in real time by the sensor node itself, in contrast to other methods based on high-scan-rate inductive loop signals, which require extensive off-line computation. It is believed that if length were used as a feature, 80% to 90% of vehicles would be correctly classified.

scholarly journals Estimation of Traffic Occupancy using Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 7291-7295
Author(s):  
M. U. Farooq ◽  
A. Ahmed ◽  
S. M. Khan ◽  
M. B. Nawaz
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Monitoring ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Percentage Difference ◽  
Novel Approach ◽  
Removal Technique

Increased traffic flow results in high road occupancy. Traffic road occupancy is often used as a parameter for the prediction of traffic conditions by traffic engineers. Although traffic monitoring systems are based on a large number of technologies, challenges are still present. Most of the methods work efficiently for free-flow traffic but not in heavy congestion. Image processing techniques are more effective than other methods, as they are based on loop sensors and detectors to monitor road traffic. A huge number of image frames are processed in image processing hence there is a need for a more efficient and low-cost image processing technique for accurate vehicle detection. In this paper, a novel approach is adopted to calculate road occupancy. The proposed framework has robust performance under road conjunction and diverse environmental conditions. A combination of image segmentation threshold technique and shadow removal technique is used. The study comprised of segmenting 1056 images extracted from recorded videos. The obtained results by image segmentation were compared with traffic road occupancy calculated manually using Autocad. A final percentage difference of 8.7 was observed.

Spinning projectile’s attitude measurement using intersection ratio of magnetic sensors

2016 ◽  
Vol 231 (5) ◽  
pp. 866-876 ◽  
Author(s):  
Jing Yu ◽  
Xiongzhu Bu ◽  
Chao Xiang ◽  
Bo Yang
Keyword(s):  
Measurement Method ◽  
Inertial Sensors ◽  
Detection System ◽  
Low Cost ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
Attitude Measurement ◽  
Characteristic Ratio ◽  
Zero Crossing ◽  
Attitude Angle

Concerning on the problem of low measuring precision of the current micro-inertial sensors, a novel attitude measurement method is proposed to dismiss the drift for remarkable attitude error. According to the output of the onboard three-axis magnetic sensor in the process of projectile flight, a low-cost attitude detection system is designed by using the intersection ratio of the sensor. First, the output model of the onboard three-axis magnetic sensor is established. The mathematical relationship between the characteristic ratio of magnetic sensor output and the pitch angle is then derived. Then, the solution and correction algorithm of the attitude angles are studied. Finally, the effectiveness of the attitude measurement method has been validated by carrying out the semi-physical experiments. The experimental results indicate that the error of attitude angles is within ±1° and the attitude angle error of the combined magnetic sensors is not cumulative. Meanwhile, the geomagnetic field strength is dispensable during the whole calculation process. Compared with the “Zero Crossing Method”, the proposed method has shown a nearly two-times higher accuracy and has no limitation of “MAGSONDE window”. What is more, this method proves to be more simple and has a doubled update rate in attitude angle calculation.

scholarly journals A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3243 ◽  
Author(s):  
Marcin Bernas ◽  
Bartłomiej Płaczek ◽  
Wojciech Korski ◽  
Piotr Loska ◽  
Jarosław Smyła ◽  
...  
Keyword(s):  
Road Traffic ◽  
Low Cost ◽  
Pedestrian Detection ◽  
Traffic Monitoring ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Active Sensors ◽  
Wide Range ◽  
Passive Sensors ◽  
Supporting Structures

This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection.

scholarly journals A Low-Cost Open Hardware System for Collecting Traffic Data Using Wi-Fi Signal Strength

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3623 ◽  
Author(s):  
Shivam Gupta ◽  
Albert Hamzin ◽  
Auriol Degbelo
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Smart Cities ◽  
Low Cost ◽  
Heavy Traffic ◽  
Traffic Monitoring ◽  
Traffic Data ◽  
Open Hardware ◽  
Loop Detectors ◽  
Hardware System

Road traffic and its impacts affect various aspects of wellbeing with safety, congestion and pollution being of significant concern in cities. Although there have been a large number of works done in the field of traffic data collection, there are several barriers which restrict the collection of traffic data at higher resolution in the cities. Installation and maintenance costs can act as a disincentive to use existing methods (e.g., loop detectors, video analysis) at a large scale and hence limit their deployment to only a few roads of the city. This paper presents an approach for vehicle counting using a low cost, simple and easily installable system. In the proposed system, vehicles (i.e., bicycles, cars, trucks) are counted by means of variations in the WiFi signals. Experiments with the developed hardware in two different scenarios—low traffic (i.e., 400 objects) and heavy traffic roads (i.e., 1000 objects)—demonstrate its ability to detect cars and trucks. The system can be used to provide estimates of vehicle numbers for streets not covered by official traffic monitoring techniques in future smart cities.

scholarly journals Real-time monitoring of traffic parameters

2020 ◽  
Author(s):  
Kirill Khazukov ◽  
Vladimir Shepelev ◽  
Tatiana Karpeta ◽  
Salavat Shabiev ◽  
Ivan Slobodin ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Video Surveillance ◽  
Network Architecture ◽  
Road Traffic ◽  
Traffic Monitoring ◽  
Quality Data ◽  
Vehicle Speed ◽  
Time Data ◽  
Surveillance Camera

Abstract This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6,000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

MagMonitor: Vehicle Speed Estimation and Vehicle Classification Through A Magnetic Sensor

2020 ◽  
pp. 1-12 ◽  
Author(s):  
Yimeng Feng ◽  
Guoqiang Mao ◽  
Bo Cheng ◽  
Changle Li ◽  
Yilong Hui ◽  
...  
Keyword(s):  
Magnetic Sensor ◽  
Vehicle Classification ◽  
Speed Estimation ◽  
Vehicle Speed

scholarly journals Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7872
Author(s):  
Donatas Miklusis ◽  
Vytautas Markevicius ◽  
Dangirutis Navikas ◽  
Mindaugas Cepenas ◽  
Juozas Balamutas ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Correlation Method ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Magnetic Sensors ◽  
Vehicle Speed ◽  
Personal Privacy ◽  
Length Estimation ◽  
Magnetic Signature ◽  
Magnetic Signatures

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.

scholarly journals Real-time monitoring of traffic parameters

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kirill Khazukov ◽  
Vladimir Shepelev ◽  
Tatiana Karpeta ◽  
Salavat Shabiev ◽  
Ivan Slobodin ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Video Surveillance ◽  
Network Architecture ◽  
Road Traffic ◽  
Traffic Monitoring ◽  
Quality Data ◽  
Vehicle Speed ◽  
Time Data ◽  
Surveillance Camera

Abstract This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

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