Application of the Convolutional Neural Networks for the Security of the Object Recognition in a Video Stream

Abstract

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.

Нейромережева Система Розпізнавання Автономера

“…На першому етапі вхідне зображення довільним чином розбивається на множину регіонів зацікавлення, які поступають на входи згорткових мереж (CNN, Convolution Neural Networks), які вирішують завдання місцезнаходження автономера на вхідному зображенні та формує на виході багатовимірний вектор ознак (4096 у нашому випадку). Отримані вектори ознак поступають на вхід класифікатора SVM (support vector machine,), лінійних класифікаторів на базі методу опорних векторів [14][15][16]. На виході Mask R-CNN нейромережі отримуємо місцеположення пластини автономера на фотозображенні автомобіля (рис.…”

Deep Learning Technology for Vehicle Recognition in Intelligent Security

“…(2) Convolutional neural network When studying cat cortex, convolutional neural network found that cat's visual system has a hierarchical structure similar to human cerebral cortex, while convolutional neural network is a hierarchical network, which has levels not found in traditional networks, and the functions and forms of layers have changed [9][10]. CNN is a kind of deep learning network structure widely used in image processing.…”