Method for determining special inserts in the video data stream for informational videos

2021 ◽  
Keyword(s):  
Data Stream ◽  
Video Data

scholarly journals Using GMSL Technology for Transmitting Stream of High-Quality Video Data

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Bohdan Andriiovych Ponomarenko
Keyword(s):  
Data Transmission ◽  
Electromagnetic Interference ◽  
Data Stream ◽  
Transmission Rate ◽  
Video Camera ◽  
Video Data ◽  
Clock Signal ◽  
Gigabit Ethernet ◽  
Advantages And Disadvantages ◽  
Camera Module

The article is devoted to the description and research of technologies for data flow transmission from video cameras designed for use in modern cars. The paper provides a theoretical analysis of the most popular wired and wireless data streaming technologies, such as Gigabit Ethernet and Wi-Fi. Analysis of the most important characteristics showed, that the mentioned technologies are not effective due to their significant shortcomings, which are critical in the production of cars, and will not allow their usage. The main disadvantages of Gigabit Ethernet technology are the lack of guarantees of full delivery of transmitted data. Given that the data obtained can be used for self-driving control systems, the technology cannot be used for transmitting the data stream to the camera, as there will be a possibility of endangering the safety of the passenger and accidents to the vehicle. Disadvantages of Wi-Fi technology include low data rates and lack of reliable protection against electromagnetic interference. Given the advantages and disadvantages of the above technologies, it was decided to abandon the possibility of their usage and consider suitable options. It is shown, that in the considered conditions, the most expedient solution for data transmission from the video camera is GMSL technology, which makes it possible to significantly increase the resistance to electromagnetic interference, the overall transmission rate, and the quality of the transmitted data. The technical features of GMSL technology, in particular SerDes technology, which is one of the main components of the above technology, are considered. For SerDes technology, the process and features of converting a parallel data stream into a serial, data transmission, and inverse data conversion into a parallel form are described. The usage of this technology makes it possible to transmit data over a distance of up to 15 meters (without significant loss of quality). Described is a method of encoding input data in the format 8b/10b. This encoding makes it possible to ensure the stability of data transmission and their overall resistance to electromagnetic interference. This is achieved by noise of the useful carrier signal. Describes the possibility of restoring the clock signal using the CDR block after receiving data on the differential pair, as it does not contain a separate line for this signal. At the same time the problem of current and voltage balance is solved. The methods of controlling the video camera module and SerDes components using I2C and UART interfaces are shown. The peculiarity of the control signals is that they can be directed in different directions due to the duplex transmission channel. The expediency of using GMSL technology in a device that implements data transmission from the camera module is considered. The advantages of using the described technology and their compliance with the established requirements are given. It is proposed to implement the ability to control the mode of operation of the camera from the control device using GMSL technology.

ONM system coding and decoding device for digital video data stream compression

2015 ◽  
Vol 13 (2) ◽  
pp. 190-196
Author(s):  
V.N. Bezrukov ◽  
◽  
A.V. Balobanov ◽  
V.G. Balobanov ◽  
◽  
...  
Keyword(s):  
Data Stream ◽  
Digital Video ◽  
Video Data

Soft computing channel optimization for compressed and raw video data stream

1998 ◽  
Author(s):  
Igor V. Ternovskiy ◽  
Andrew A. Kostrzewski ◽  
Aleksandr Devivye ◽  
Tomasz P. Jannson ◽  
Freddie S. Lin
Keyword(s):  
Soft Computing ◽  
Data Stream ◽  
Video Data

scholarly journals Design of Audio Embedding and De-embedding for 3G-SDI Based on FPGA

2018 ◽  
Vol 173 ◽  
pp. 03021
Author(s):  
Yaqing Liu ◽  
Lunhui Deng
Keyword(s):  
Theoretical Basis ◽  
Data Stream ◽  
Auxiliary Information ◽  
Audio Signal ◽  
Video Data ◽  
Digital Audio ◽  
Audio Signals ◽  
Audio Data ◽  
Video And Audio ◽  
The Cost

This design introduces the theoretical basis of digital audio embedding and de-embedding, and proposes a solution that Verilog language can be used to achieve 3G-SDI audio embedding and de-embedding. SDI video and audio data are input to the FPGA, and the audio signals can be embedded in the SDI line blanking after processing. Moreover, some auxiliary information is embedded in the SDI data, when you need these auxiliary information, you need to use the audio de-embedding process. The process of audio de-embedding is inversed with the process of embedding. It has been proved through practice that this scheme can effectively embed digital audio in SDI data stream, synchronize audio and video data, and can de-embed audio signal. The design is very versatile and can improve the efficiency of the design, thus effectively reducing the cost of the product.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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