CVSS

2019 ◽  
pp. 19-32
Author(s):  
Lei Zhou ◽  
Wei Qi Yan ◽  
Yun Shu ◽  
Jian Yu
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Virtual Machine ◽  
Surveillance System ◽  
Video Stream ◽  
Visual Surveillance ◽  
Video Transcoding ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
And Storage

A large amount of surveillance videos and images need sufficient storage. In this article, an architecture of cloud-based surveillance systems and its modules will be designed, the Cloud-based Visual Surveillance System (CVSS) will be implemented on a private cloud using a Virtual Machine (VM). The users are able to link their cameras to the CVSS system so that the goal of this design can be achieved. The authors' CVSS system is able to push notification messages of captured videos to receivers, and their users could receive a surveillance video along with its events. The CVSS system fully makes use of the merits of cloud computing, which make it more advanced as stated in the evaluation section of this article. The contributions of this article are to be implemented in the CVSS system with: (1) video stream input, (2) intelligent visual surveillance, (3) real-time video transcoding and storage, (4) message pushing and media streaming output.

CVSS

2018 ◽  
Vol 10 (1) ◽  
pp. 79-91 ◽  
Author(s):  
Lei Zhou ◽  
Wei Qi Yan ◽  
Yun Shu ◽  
Jian Yu
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Surveillance System ◽  
Video Stream ◽  
Visual Surveillance ◽  
Video Transcoding ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Private Cloud ◽  
And Storage

A large amount of surveillance videos and images need sufficient storage. In this article, an architecture of cloud-based surveillance systems and its modules will be designed, the Cloud-based Visual Surveillance System (CVSS) will be implemented on a private cloud using a Virtual Machine (VM). The users are able to link their cameras to the CVSS system so that the goal of this design can be achieved. The authors' CVSS system is able to push notification messages of captured videos to receivers, and their users could receive a surveillance video along with its events. The CVSS system fully makes use of the merits of cloud computing, which make it more advanced as stated in the evaluation section of this article. The contributions of this article are to be implemented in the CVSS system with: (1) video stream input, (2) intelligent visual surveillance, (3) real-time video transcoding and storage, (4) message pushing and media streaming output.

scholarly journals The Utilization of Cloud Computing as Virtual Machine

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
Real Time ◽  
Virtual Machine ◽  
Significant Role ◽  
Relevant Information ◽  
The Internet ◽  
Conventional System ◽  
The World ◽  
And Storage

Cloud computing has a significant role in the world of information technology, in particular for the retrieval and storage of relevant information. Globally, it creates a more dynamic, fast and precise function. In cloud computing, all information is stored online and does not require hardware like a conventional system. All are available globally on the internet network. It also eliminates the huge cost of buying and managing all existing hardware. Cloud computing has a quick access speed and can be managed in real-time. The data on the server can be easily arranged and distributed to people in need. It will lead the users to use and adapt this technology quickly. This technology generates speed and reliability more than the previous technology.

Edge Artificial Intelligence: A Multi-Camera Video Surveillance Application

2021 ◽  
Author(s):  
Daniele Berardini ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Sara Moccia
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Video Surveillance ◽  
Cost Effective ◽  
Fast Response ◽  
Video Stream ◽  
Single Shot ◽  
Central Unit ◽  
Surveillance Video ◽  
People Detection

Abstract Nowadays, video surveillance has a crucial role. Analyzing surveillance videos is, however, a time consuming and tiresome procedure. In the last years, artificial intelligence paved the way for automatic and accurate surveillance-video analysis. In parallel to the development of artificial-intelligence methodologies, edge computing is becoming an active field of research with the final goal to provide cost-effective and real time deployment of the developed methodologies. In this work, we present an edge artificial intelligence application to video surveillance. Our approach relies on a set of four IP cameras, which acquire video frames that are processed on the edge using the NVIDIA® Jetson Nano. A state-of-the-art deep-learning model, called Single Shot multibox Detector (SSD) MobileNetV2 network, is used to perform object and people detection in real-time. The proposed infrastructure obtained an inference speed of ∼10.0 Frames per Second (FPS) for each parallel video stream. These results prompt the possibility of translating our work into a real word scenario. The integration of the presented application into a wider monitoring system with a central unit could bring benefits to the overall infrastructure. Indeed our application could send only video-related high-level information to the central unit, allowing it to combine information with data coming from other sensing devices without unuseful data overload. This would ensure a fast response in case of emergency or detected anomalies. We hope this work will contribute to stimulate the research in the field of edge artificial intelligence for video surveillance.

Confidentiality and Safekeeping Problems and Techniques in Fog Computing

2021 ◽  
pp. 1855-1873
Author(s):  
Nida Kauser Khanum ◽  
Pankaj Lathar ◽  
G. M. Siddesh
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Fog Computing ◽  
Video Stream ◽  
Security And Privacy ◽  
Surveillance Video ◽  
Speech Data ◽  
Real Time Applications ◽  
Privacy Issues ◽  
Intrusion Detection And Prevention

Fog computing is an extension of cloud computing, and it is one of the most important architypes in the current world. Fog computing is like cloud computing as it provides data storage, computation, processing, and application services to end-users. In this chapter, the authors discuss the security and privacy issues concerned with fog computing. The issues present in cloud are also inherited by fog computing, but the same methods available for cloud computing are not applicable to fog computing due to its decentralized nature. The authors also discuss a few real-time applications like healthcare systems, intelligent food traceability, surveillance video stream processing, collection, and pre-processing of speech data. Finally, the concept of decoy technique and intrusion detection and prevention technique is covered.

Utility of Ambulance Data for Real-Time Syndromic Surveillance: A Pilot in the West Midlands Region, United Kingdom

2017 ◽  
Vol 32 (6) ◽  
pp. 667-672 ◽  
Author(s):  
Dan Todkill ◽  
Paul Loveridge ◽  
Alex J. Elliot ◽  
Roger A. Morbey ◽  
Obaghe Edeghere ◽  
...  
Keyword(s):  
United Kingdom ◽  
Real Time ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Lessons Learned ◽  
Surveillance Systems ◽  
Preliminary Assessment ◽  
The West ◽  
Syndromic Surveillance System ◽  
West Midlands

AbstractIntroductionThe Public Health England (PHE; United Kingdom) Real-Time Syndromic Surveillance Team (ReSST) currently operates four national syndromic surveillance systems, including an emergency department system. A system based on ambulance data might provide an additional measure of the “severe” end of the clinical disease spectrum. This report describes the findings and lessons learned from the development and preliminary assessment of a pilot syndromic surveillance system using ambulance data from the West Midlands (WM) region in England.Hypothesis/ProblemIs an Ambulance Data Syndromic Surveillance System (ADSSS) feasible and of utility in enhancing the existing suite of PHE syndromic surveillance systems?MethodsAn ADSSS was designed, implemented, and a pilot conducted from September 1, 2015 through March 1, 2016. Surveillance cases were defined as calls to the West Midlands Ambulance Service (WMAS) regarding patients who were assigned any of 11 specified chief presenting complaints (CPCs) during the pilot period. The WMAS collected anonymized data on cases and transferred the dataset daily to ReSST, which contained anonymized information on patients’ demographics, partial postcode of patients’ location, and CPC. The 11 CPCs covered a broad range of syndromes. The dataset was analyzed descriptively each week to determine trends and key epidemiological characteristics of patients, and an automated statistical algorithm was employed daily to detect higher than expected number of calls. A preliminary assessment was undertaken to assess the feasibility, utility (including quality of key indicators), and timeliness of the system for syndromic surveillance purposes. Lessons learned and challenges were identified and recorded during the design and implementation of the system.ResultsThe pilot ADSSS collected 207,331 records of individual ambulance calls (daily mean=1,133; range=923-1,350). The ADSSS was found to be timely in detecting seasonal changes in patterns of respiratory infections and increases in case numbers during seasonal events.ConclusionsFurther validation is necessary; however, the findings from the assessment of the pilot ADSSS suggest that selected, but not all, ambulance indicators appear to have some utility for syndromic surveillance purposes in England. There are certain challenges that need to be addressed when designing and implementing similar systems.TodkillD, LoveridgeP, ElliotAJ, MorbeyRA, EdeghereO, Rayment-BishopT, Rayment-BishopC, ThornesJE, SmithG. Utility of ambulance data for real-time syndromic surveillance: a pilot in the West Midlands region, United Kingdom. Prehosp Disaster Med. 2017;32(6):667–672.

scholarly journals Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5706
Author(s):  
Muhammad Shuaib Qureshi ◽  
Muhammad Bilal Qureshi ◽  
Muhammad Fayaz ◽  
Muhammad Zakarya ◽  
Sheraz Aslam ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Real Time ◽  
Large Scale ◽  
Smart Devices ◽  
Budget Constraints ◽  
Smart Systems ◽  
Data Intensive ◽  
Cost Efficient ◽  
And Storage

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

An Intelligent Video Surveillance System for Android Smart Phone

2013 ◽  
Vol 850-851 ◽  
pp. 884-888 ◽  
Author(s):  
Gang Yang ◽  
Xin Tan ◽  
Yong Rui Zhang
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Smart Phone ◽  
Image Data ◽  
Gaussian Mixture ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Surveillance Technology

Video surveillance technology is playing an important role, and it is widely used in some fields. With the popularity of Android OS, it draws researchers attention to increase the development of video surveillance systems on the platform. This paper presents a smart real-time video surveillance system based on Android smart phone. This system detects moving object by using improved GMM (Gaussian Mixture Mode) algorithm, recognizes invading human with cascade classifier, processes image data with coder & decoder, transmits data over RTP (Real-time Transport Protocol). It also applies some methods to improve the accuracy of moving object detection and recognition, speed up recognition process. The experimental evidences show that it can realize real-time video surveillance and smart alarm.

Analysis Engine Based on Cloud Computing

2014 ◽  
Vol 602-605 ◽  
pp. 3478-3480
Author(s):  
Shan Hong Zhu ◽  
Wei Liu
Keyword(s):  
Cloud Computing ◽  
Behavior Analysis ◽  
Real Time ◽  
User Behavior ◽  
User Behavior Analysis ◽  
Efficient Processing ◽  
Processing And Storage ◽  
Mining Model ◽  
Analysis Engine ◽  
And Storage

In this paper come up with the idea of user behavior analysis engine, the combination of the static analysis of user behavior and real-time monitoring, real-time acquisition of Web log and user to access the context information of the page, apply to the improved data mining model analysis, which based on cloud computing technology, meanwhile efficient processing and storage, cloud database test showed that, the system can significantly improve the effect and efficiency of user behavior analysis.

ANALYSIS OF HUMAN WALKING TRAJECTORIES FOR SURVEILLANCE

2004 ◽  
Vol 01 (02) ◽  
pp. 169-189
Author(s):  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Real Time ◽  
Similarity Measure ◽  
Real Life ◽  
Visual Surveillance ◽  
Longest Common Subsequence ◽  
Human Walking ◽  
Support Vector ◽  
Surveillance Systems ◽  
Boundary Values ◽  
Variable Boundary

Surveillance of public places has become a worldwide concern in recent years. The ability to identify abnormal human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of abnormal and suspicious human walking patterns is an important step towards the achievement of this goal. In this research, we develop an intelligent visual surveillance system that can classify normal and abnormal human walking trajectories in outdoor environments by learning from demonstration. The system takes into account both the local and global characteristics of the observed trajectories and is able to identify their normality in real-time. By utilizing support vector learning and a similarity measure based on hidden Markov models, the developed system has produced satisfactory results on real-life data during testing. Moreover, we utilize the approach of longest common subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks.

Sign in / Sign up

Export Citation Format

Share Document