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.

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.

scholarly journals Real-time monitoring of traffic parameters

2020 ◽  
Author(s):  
Kirill Hazukov ◽  
Vladimir Shepelev ◽  
Tatiana Carpeta ◽  
Salavat Shabiev ◽  
Ivan Slobodin ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Traffic Flow ◽  
Network Architecture ◽  
Road Traffic ◽  
Quality Data ◽  
Transport Systems ◽  
Source Image ◽  
Vehicle Speed ◽  
On The Road

Abstract This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the surveillance camera data. The purpose of the paper is to develop a system to collect data on the traffic flow structure and to determine the traffic flow speed and direction in real-time. Our solution is based on the use of the YOLOv3 neural network architecture and open-source tracker SORT. To increase the accuracy of detection and classification, we used multi-scale prediction with an increased number of anchors. To determine the speed, we used a matrix of the perspective transformation of the source image to geographical coordinates. To train the neural network, we marked over 6,000 images and performed augmentation, which allowed us to increase the dataset to 60,000 images. Checking the system at night and in the day showed an absolute percentage accuracy of counting vehicles 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 2.74 m/s. The presented study allows us to generate big data for the intelligent transport systems decision-making system in real-time and to lower the requirements for peripheral equipment.

scholarly journals FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7399
Author(s):  
Ming-Hwa Sheu ◽  
S M Salahuddin Morsalin ◽  
Jia-Xiang Zheng ◽  
Shih-Chang Hsia ◽  
Cheng-Jian Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Sigmoid Function ◽  
Real Time Processing ◽  
Scale Choice ◽  
Training Stage

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Yanfang Wang ◽  
Saeed Salehi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Field Data ◽  
Ann Model ◽  
Time Data ◽  
Neural Network Approach ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Artificial Neural Network Ann ◽  
Pump Pressure

Real-time drilling optimization improves drilling performance by providing early warnings in operation Mud hydraulics is a key aspect of drilling that can be optimized by access to real-time data. Different from the investigated references, reliable prediction of pump pressure provides an early warning of circulation problems, washout, lost circulation, underground blowout, and kicks. This will help the driller to make necessary corrections to mitigate potential problems. In this study, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab. Following the determination of the optimum model, the sensitivity analysis of input parameters on the created model was investigated by using forward regression method. Next, the remaining data from the selected well samples was applied for simulation to verify the quality of the developed model. The novelty is this paper is validation of computer models with actual field data collected from an operator in LA. The simulation result was promising as compared with collected field data. This model can accurately predict pump pressure versus depth in analogous formations. The result of this work shows the potential of the approach developed in this work based on NN models for predicting real-time drilling hydraulics.

The Analysis and Design of the Dynamic Information Service System Network Architecture of the SOA Based on WAVE Technology

2014 ◽  
Vol 1044-1045 ◽  
pp. 1380-1383
Author(s):  
Guang Li Yin
Keyword(s):  
Network Architecture ◽  
High Speed ◽  
Service System ◽  
Road Traffic ◽  
Rapid Development ◽  
Information Service ◽  
Advanced Technology ◽  
Living Standard ◽  
Vehicle Speed ◽  
Analysis And Design

Safety problem is one of the most attention and concern of driving. This paper in the high-speed on the road cars and car, car and road communications, vehicle real-time status, through the network information service system integration on a platform, on the use of related technologies are analyzed, the design of the software system based on SOA architecture.Keywords: network, GPS module, SOA cross platformI. IntorductionWith the development of science and technology and the improvement of people's living standard, Car popularity rate is high, it's hard to believe, families has two or three car. Whether it is the bus or private car is such rapid development, this will bring a lot of problems in road traffic, such as traffic congestion, traffic accident. These problems affect the normal life and travel, it is necessary to carry out management and provide information service for road use advanced technology. Using mobile phone GPS positioning module can obtain the vehicle speed and the basic information, through processing and optimization of information service system, the analysis of data useful, so as to divert traffic, both for the convenience of the user, but also improve the expressway management ability.

scholarly journals Real-time diameter of the fetal aorta from ultrasound

2019 ◽  
Vol 32 (11) ◽  
pp. 6735-6744
Author(s):  
Nicoló Savioli ◽  
Enrico Grisan ◽  
Silvia Visentin ◽  
Erich Cosmi ◽  
Giovanni Montana ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Ultrasound Images ◽  
Imaging Features ◽  
Execution Speed ◽  
The Mean ◽  
Temporal Redundancy ◽  
Gated Recurrent Unit

AbstractThe automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. This article presents an attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. The solution is investigated with a cohort of 25 ultrasound sequences acquired during the third-trimester pregnancy check, and with 1000 synthetic sequences. In the extraction of features, it is shown that a shallow CNN outperforms two other deep CNNs with both the real and synthetic cohorts, suggesting that echocardiographic features are optimally captured by a reduced number of CNN layers. The proposed architecture, working with the shallow CNN, reaches an accuracy substantially superior to previously reported methods, providing an average reduction of the mean squared error from 0.31 (state-of-the-art) to 0.09 $$\mathrm{mm}^2$$mm2, and a relative error reduction from 8.1 to 5.3%. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real-time clinical use.

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