Optimization of Storage Method for Video Segment Captured by Embedded System in Camera

2011 ◽  
Vol 268-270 ◽  
pp. 2116-2120
Author(s):  
Wei Yan
Keyword(s):  
Embedded System ◽  
Video Surveillance ◽  
Video Processing ◽  
Continuous Monitoring ◽  
Video Data ◽  
Video Segment ◽  
Fixed Size ◽  
Storage Method ◽  
The Embedded System ◽  
Cache Capacity

Video processing and cache capacity in embedded system in camera is the key to decide intelligence of shooting system. In particular, to meet the demand for continuous monitoring in video surveillance environment, the embedded system must be able to store the video in case of failure in network or server. In this paper, a storage method for videos with fixed size is proposed, which can effectively restrain occurrence of fragment during storage and raise I/O performance while ensuring continuity of monitoring. Furthermore, storage of video data based on H.264/AVC encoding system and its optimization are also discussed.

Research of Intelligent Video Surveillance System Based on the 3G Technology

2012 ◽  
Vol 614-615 ◽  
pp. 1377-1380
Author(s):  
Yong Li Ma
Keyword(s):  
Embedded System ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Data ◽  
Video Surveillance System ◽  
Intelligent Video Surveillance ◽  
3G Network ◽  
Intelligent Video Surveillance System ◽  
The Embedded System ◽  
Remote Viewing

Face to the questions in the traditional video monitoring system, this paper proposed the intelligent video surveillance system based on the 3G technology, developed by the embedded system. The system can realize the information transmitted through the 3G network. And the system has the function of remote real-time audio monitoring, remote viewing and download the video data acquisition, and message security alarm.

scholarly journals SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 911 ◽  
Author(s):  
Md Azher Uddin ◽  
Aftab Alam ◽  
Nguyen Anh Tu ◽  
Md Siyamul Islam ◽  
Young-Koo Lee
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Video Surveillance ◽  
Video Processing ◽  
Large Scale ◽  
Distributed Processing ◽  
Content Management ◽  
Video Data ◽  
Video Analytics ◽  
Intelligent Video Surveillance

In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.

scholarly journals A Low Energy Consumption Storage Method for Cloud Video Surveillance Data Based on SLA Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yonghua Xiong ◽  
Chengda Lu ◽  
Min Wu ◽  
Keyuan Jiang ◽  
Dianhong Wang
Keyword(s):  
Energy Consumption ◽  
Data Storage ◽  
Video Surveillance ◽  
Data Centers ◽  
Video Data ◽  
Low Energy ◽  
Access Time ◽  
Storage Method ◽  
Low Energy Consumption ◽  
Time Periods

With the continuous expansion of the amount of data with time in mobile video applications such as cloud video surveillance (CVS), the increasing energy consumption in video data centers has drawn widespread attention for the past several years. Addressing the issue of reducing energy consumption, we propose a low energy consumption storage method specially designed for CVS systems based onthe service level agreement (SLA) classification. A novel SLA with an extra parameter of access time period is proposed and then utilized as a criterion for dividing virtual machines (VMs) and data storage nodes into different classifications. Tasks can be scheduled in real time for running on the homologous VMs and data storage nodes according to their access time periods. Any nodes whose access time periods do not encompass the current time will be placed into the energy saving state while others are in normal state with the capability of undertaking tasks. As a result, overall electric energy consumption in data centers is reduced while the SLA is fulfilled. To evaluate the performance, we compare the method with two related approaches using the Hadoop Distributed File System (HDFS). The results show the superiority and effectiveness of our method.

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 Multiple Narrowband Interferences Characterization, Detection and Mitigation Using Simplified Welch Algorithm and Notch Filtering

2021 ◽  
Vol 11 (3) ◽  
pp. 1331
Author(s):  
Mohammad Hossein Same ◽  
Gabriel Gleeton ◽  
Gabriel Gandubert ◽  
Preslav Ivanov ◽  
Rene Jr Landry
Keyword(s):  
Power Spectrum ◽  
Embedded System ◽  
Radio Frequency ◽  
Satellite Communication ◽  
Signal To Noise Ratio ◽  
Center Frequency ◽  
Infinite Impulse Response ◽  
Frequency Variation ◽  
Welch Method ◽  
The Embedded System

By increasing the demand for radio frequency (RF) and access of hackers and spoofers to low price hardware and software defined radios (SDR), radio frequency interference (RFI) became a more frequent and serious problem. In order to increase the security of satellite communication (Satcom) and guarantee the quality of service (QoS) of end users, it is crucial to detect the RFI in the desired bandwidth and protect the receiver with a proper mitigation mechanism. Digital narrowband signals are so sensitive into the interference and because of their special power spectrum shape, it is hard to detect and eliminate the RFI from their bandwidth. Thus, a proper detector requires a high precision and smooth estimation of input signal power spectral density (PSD). By utilizing the presented power spectrum by the simplified Welch method, this article proposes a solid and effective algorithm that can find all necessary interference parameters in the frequency domain while targeting practical implantation for the embedded system with minimum complexity. The proposed detector can detect several multi narrowband interferences and estimate their center frequency, bandwidth, power, start, and end of each interference individually. To remove multiple interferences, a chain of several infinite impulse response (IIR) notch filters with multiplexers is proposed. To minimize damage to the original signal, the bandwidth of each notch is adjusted in a way that maximizes the received signal to noise ratio (SNR) by the receiver. Multiple carrier wave interferences (MCWI) is utilized as a jamming attack to the Digital Video Broadcasting-Satellite-Second Generation (DVB-S2) receiver and performance of a new detector and mitigation system is investigated and validated in both simulation and practical tests. Based on the obtained results, the proposed detector can detect a weak power interference down to −25 dB and track a hopping frequency interference with center frequency variation speed up to 3 kHz. Bit error ratio (BER) performance shows 3 dB improvement by utilizing new adaptive mitigation scenario compared to non-adaptive one. Finally, the protected DVB-S2 can receive the data with SNR close to the normal situation while it is under the attack of the MCWI jammer.

scholarly journals The Design of a 2D Graphics Accelerator for Embedded Systems

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 469
Author(s):  
Hyun Woo Oh ◽  
Ji Kwang Kim ◽  
Gwan Beom Hwang ◽  
Seung Eun Lee
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Line Drawing ◽  
Cmos Process ◽  
Embedded Processor ◽  
Processor Core ◽  
Field Programmable ◽  
The Embedded System ◽  
Graphics Processing

Recently, advances in technology have enabled embedded systems to be adopted for a variety of applications. Some of these applications require real-time 2D graphics processing running on limited design specifications such as low power consumption and a small area. In order to satisfy such conditions, including a specific 2D graphics accelerator in the embedded system is an effective method. This method reduces the workload of the processor in the embedded system by exploiting the accelerator. The accelerator assists the system to perform 2D graphics processing in real-time. Therefore, a variety of applications that require 2D graphics processing can be implemented with an embedded processor. In this paper, we present a 2D graphics accelerator for tiny embedded systems. The accelerator includes an optimized line-drawing operation based on Bresenham’s algorithm. The optimized operation enables the accelerator to deal with various kinds of 2D graphics processing and to perform the line-drawing instead of the system processor. Moreover, the accelerator also distributes the workload of the processor core by removing the need for the core to access the frame buffer memory. We measure the performance of the accelerator by implementing the processor, including the accelerator, on a field-programmable gate array (FPGA), and ascertaining the possibility of realization by synthesizing using the 180 nm CMOS process.

Reform of embedded system experiment course based on engineering education accreditation

2020 ◽  
pp. 002072092091815
Author(s):  
Yong Luo ◽  
Shuai-Bing Qin ◽  
Dong-Shu Wang
Keyword(s):  
Education Reform ◽  
Embedded System ◽  
Engineering Education ◽  
Assessment Method ◽  
Teaching Model ◽  
Electronic Information ◽  
Course Content ◽  
Practical Operation ◽  
System Experiment ◽  
The Embedded System

With the continuous development of engineering education accreditation in China, its concept has had a profound impact on the reform of various majors in higher education. Using the idea of engineering education accreditation, this paper discusses the main problems in the implementation of embedded experimental courses of electronic information majors and proposes related education reform programs. Taking the embedded system experiment course of the automation major and embedded system major of Zhengzhou University as examples, the course has carried out research on the aspects of teaching model, experimental course content, scientific assessment method, etc., and proposed corresponding improvement methods to achieve better effect. The practical operation result has proved that the embedded system experiment course of the automation major and embedded system major improved the students’ ability and met the requirements of professional accreditation.

Structure of an Embedded System Software Platform for Plate Recognition System Based on Applied Mechanics

2012 ◽  
Vol 460 ◽  
pp. 266-270
Author(s):  
Xing Wu Sun ◽  
Yu Chen ◽  
Ai Fei Wang
Keyword(s):  
Embedded System ◽  
Recognition System ◽  
Cmos Camera ◽  
Camera System ◽  
New Thought ◽  
Development Platform ◽  
Embedded Linux System ◽  
Acquisition Device ◽  
The Embedded System ◽  
And Control

According to the shortcomings of large volume and high cost about the plate recognition system, an embedded plate recognition system is developed based on the ARM11 processor at lower costs. Taking the embedded Linux system as the software development platform, the system uses graphical user interface to operate and control the machine. Using CMOS camera system as image acquisition device, the system adopts HSV algorithm to realize the image classification on the platform of the embedded plate recognition system. The experimental results show that the embedded system runs stably, can realize the plate classification by color, and has the advantages of small size, low power consumption, convenience for using and so on. The embedded system provides a new thought for plate recognition.

Design and Implementation of Contour Tracing Image Recognition System

2014 ◽  
Vol 543-547 ◽  
pp. 2209-2212
Author(s):  
Chun Hua Xiong ◽  
You Jie Zhou ◽  
Gao Jun An ◽  
Chang Bo Lu
Keyword(s):  
Embedded System ◽  
Image Recognition ◽  
Integrated Design ◽  
Recognition System ◽  
Image System ◽  
Intelligent Monitoring ◽  
Contour Tracing ◽  
The Embedded System ◽  
Imaging Sensor ◽  
Very High

Based on the existing contour tracing image recognition technology, combining the embedded system technology and the computer storage control technology, the author makes an integrated design, adopts the image processing chip, USB controller, the imaging sensor and other hardware circuits and develops an intelligent image system. The system can make real-time monitoring the size and change of millimeter-sized irregular target objects. Its applicable value in the fields such as intelligent monitoring of oil equipment, medical imaging and criminal investigation is very high.

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