Real-time correlation processing of television communication channel video data

2001 ◽  
Author(s):  
Alexander Larkin ◽  
Leonid V. Volkov
Keyword(s):  
Real Time ◽  
Communication Channel ◽  
Time Correlation ◽  
Video Data ◽  
Correlation Processing ◽  
Television Communication

FPGA Implement of Multi-Channel Real-Time Correlation Processing System

2013 ◽  
Vol 303-306 ◽  
pp. 1925-1929
Author(s):  
Xin Liu ◽  
Da Jun Sun ◽  
Ting Ting Teng ◽  
Yuan Tian
Keyword(s):  
Real Time ◽  
Processing System ◽  
Large Data ◽  
Time Correlation ◽  
Correlation Processing ◽  
Parallel Mode ◽  
Data Bus ◽  
Low Efficiency ◽  
System Controller ◽  
Processing Architecture

The traditional real-time correlation processing system in FPGA is implemented in parallel mode. It has disadvantages such as high FPGA resource-consuming, low efficiency and poor flexibility. A time-multiplexed processing architecture takes NIOS processor as system controller, connected with preprocessing module, sliding-correlation processor and memories by Avalon data bus. The transmission of large data block out of sliding-correlation processor employs DMA method for its controlling flexibility, the data transmission between computing units and memory units within the processor employs directly memory access to minimum time delay.

Research on Video Encryption and Privacy Protection in Cloud Monitoring System Based on H.264

2019 ◽  
Vol 12 ◽  
Author(s):  
Qingtao Wu ◽  
Zaihui Cao
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Video Data ◽  
Encryption Algorithm ◽  
Management Tool ◽  
Video Encryption ◽  
Wireless Multimedia ◽  
Monitoring Technology ◽  
Security Issues ◽  
Cloud Monitoring

: Cloud monitoring technology is an important maintenance and management tool for cloud platforms.Cloud monitoring system is a kind of network monitoring service, monitoring technology and monitoring platform based on Internet. At present, the monitoring system is changed from the local monitoring to cloud monitoring, with the flexibility and convenience improved, but also exposed more security issues. Cloud video may be intercepted or changed in the transmission process. Most of the existing encryption algorithms have defects in real-time and security. Aiming at the current security problems of cloud video surveillance, this paper proposes a new video encryption algorithm based on H.264 standard. By using the advanced FMO mechanism, the related macro blocks can be driven into different Slice. The encryption algorithm proposed in this paper can encrypt the whole video content by encrypting the FMO sub images. The method has high real-time performance, and the encryption process can be executed in parallel with the coding process. The algorithm can also be combined with traditional scrambling algorithm, further improve the video encryption effect. The algorithm selects the encrypted part of the video data, which reducing the amount of data to be encrypted. Thus reducing the computational complexity of the encryption system, with faster encryption speed, improve real-time and security, suitable for transfer through mobile multimedia and wireless multimedia network.

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

scholarly journals A Real-Time Monitoring Platform for Distributed Energy Resources in a Microgrid—Pilot Study in Oman

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1803
Author(s):  
Nasser Hosseinzadeh ◽  
Ahmed Al Maashri ◽  
Naser Tarhuni ◽  
Abdelsalam Elhaffar ◽  
Amer Al-Hinai
Keyword(s):  
Real Time ◽  
Communication Channel ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Small Scale ◽  
Area Network ◽  
Distributed Energy ◽  
Real Time Monitoring ◽  
Holistic View ◽  
Monitoring Platform

This article presents the development of a platform for real-time monitoring of multi-microgrids. A small-scale platform has been developed and implemented as a prototype, which takes data from various types of devices located at a distance from each other. The monitoring platform is interoperable, as it allows several protocols to coexist. While the developed prototype is tested on small-scale distributed energy resources (DERs), it is done in a way to extend the concept for monitoring several microgrids in real scales. Monitoring strategies were developed for DERs by making a customized two-way communication channel between the microgrids and the monitoring center using a long-range bridged wireless local area network (WLAN). In addition, an informative and easy-to-use software dashboard was developed. The dashboard shows real-time information and measurements from the DERs—providing the user with a holistic view of the status of the DERs. The proposed system is scalable, modular, facilitates the interoperability of various types of inverters, and communicates data over a secure communication channel. All these features along with its relatively low cost make the developed real-time monitoring platform very useful for online monitoring of smart microgrids.

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