scholarly journals APPLICATION OF THE CONVOLUTIONAL NEURAL NETWORKS FOR THE SECURITY OF THE OBJECT RECOGNITION IN A VIDEO STREAM

2020 ◽  
Vol 4 (8) ◽  
pp. 97-112
Author(s):  
Danylo Svatiuk ◽  
Oksana Svatiuk ◽  
Oleksandr Belei
Keyword(s):  
Operating System ◽  
Neural Networks ◽  
Real Time ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Video Stream ◽  
License Plate ◽  
License Plate Recognition ◽  
Android Operating System

The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.

scholarly journals Deep learning Convolutional Neural Network for Unconstrained License Plate Recognition

2019 ◽  
Vol 255 ◽  
pp. 05002
Author(s):  
Pang Yee Yong ◽  
Ong Chee Hau ◽  
Sim Hiew Moi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
License Plate ◽  
License Plate Recognition ◽  
Statistical Research ◽  
Automatic Vehicle Identification ◽  
Vehicle Information

The evolve of neural networks algorithm into deep learning convolutional neural networks seems like the next generation for object detection. This algorithm works has a significantly better accuracy and did not tied to any particular aspect ratio. License plate and traffic signs detection and recognition have a number of different applications relevant for transportation systems, such as traffic monitoring, detection of stolen vehicles, driver navigation support or any statistical research. An exponential increase in number of vehicles necessitates the use of automated systems to maintain vehicle information. The information is highly required for both management of traffic as well as reduction of crime. Number plate recognition is an effective way for automatic vehicle identification. A number of methods have been proposed, but only for particular cases and working under constraints (e.g. known text direction or high resolution). Deep learning convolutional neural networks work well especially in handles occlusion/rotation better, therefore we believe this approach is able to provide a better solution to the unconstrained license plate recognition problem.

scholarly journals Real-Time Face Mask Detector Using Convolutional Neural Networks Amidst COVID-19 Pandemic

2021 ◽  
Author(s):  
Efstratios Kontellis ◽  
Christos Troussas ◽  
Akrivi Krouska ◽  
Cleo Sgouropoulou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Everyday Life ◽  
Real Time ◽  
Image Recognition ◽  
Deep Neural Network ◽  
Face Mask ◽  
Video Stream ◽  
Human Face ◽  
Detection Model

The COVID-19 pandemic provoked many changes in our everyday life. For instance, wearing protective face masks has become a new norm and is an essential measure, having been imposed by countries worldwide. As such, during these times, people must wear masks to enter buildings. In view of this compelling need, the objective of this paper is to create a real-time face mask detector that uses image recognition technology to identify: (i) if it can detect a human face in a video stream and (ii) if the human face, which was detected, was wearing an object that it looked like a face mask and if it was properly worn. Our face mask detection model is using OpenCV Deep Neural Network (DNN), TensorFlow and MobileNetV2 architecture as an image classifier and after training, achieved 99.64% of accuracy.

scholarly journals Real time mobile based license plate recognition system with neural networks

2020 ◽  
Vol 1502 ◽  
pp. 012032
Author(s):  
Connie Liew ◽  
Chin Kim On ◽  
Rayner Alfred ◽  
Tan Tse Guan ◽  
Patricia Anthony
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition

scholarly journals GNSS Positioning Using Mobile Devices with the Android Operating System

2020 ◽  
Vol 9 (4) ◽  
pp. 220 ◽  
Author(s):  
Paolo Dabove ◽  
Vincenzo Di Pietra ◽  
Marco Piras
Keyword(s):  
Operating System ◽  
Real Time ◽  
Mobile Devices ◽  
Satellite System ◽  
Post Processing ◽  
Mass Market ◽  
Phase Measurements ◽  
Accuracy And Precision ◽  
Android Operating System ◽  
Global Navigation Satellite

The access and the use of the global navigation satellite system (GNSS) pseudo-range and carrier-phase measurements mobile devices as smartphones and tablets with an Android operating system has transformed the concept of accurate positioning with mobile devices. In this work, the comparison of positioning performances obtained with a smartphone and an external mass-market GNSS receiver both in real-time and post-processing is made. Particular attention is also paid to accuracy and precision of positioning results, also analyzing the possibility of estimating the phase ambiguities as integer values (fixed positioning) that it is still challenging for mass-market devices. The precisions and accuracies obtained with the mass-market receiver were about 5 cm and 1 cm both for real-time and post-processing solutions, respectively, while those obtained with a smartphone were slightly worse (few meters in some cases) due to the noise of its measurements.

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