47,432 research outputs found
Hierarchical video surveillance architecture: a chassis for video big data analytics and exploration
There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems âexposeâ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT)
A Large-scale Distributed Video Parsing and Evaluation Platform
Visual surveillance systems have become one of the largest data sources of
Big Visual Data in real world. However, existing systems for video analysis
still lack the ability to handle the problems of scalability, expansibility and
error-prone, though great advances have been achieved in a number of visual
recognition tasks and surveillance applications, e.g., pedestrian/vehicle
detection, people/vehicle counting. Moreover, few algorithms explore the
specific values/characteristics in large-scale surveillance videos. To address
these problems in large-scale video analysis, we develop a scalable video
parsing and evaluation platform through combining some advanced techniques for
Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed
Filesystem (HDFS). Also, a Web User Interface is designed in the system, to
collect users' degrees of satisfaction on the recognition tasks so as to
evaluate the performance of the whole system. Furthermore, the highly
extensible platform running on the long-term surveillance videos makes it
possible to develop more intelligent incremental algorithms to enhance the
performance of various visual recognition tasks.Comment: Accepted by Chinese Conference on Intelligent Visual Surveillance
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Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
DESIGN FRAMEWORK FOR INTERNET OF THINGS BASED NEXT GENERATION VIDEO SURVEILLANCE
Modern artificial intelligence and machine learning opens up new era towards video
surveillance system. Next generation video surveillance in Internet of Things (IoT) environment is
an emerging research area because of high bandwidth, big-data generation, resource constraint
video surveillance node, high energy consumption for real time applications. In this thesis, various
opportunities and functional requirements that next generation video surveillance system should
achieve with the power of video analytics, artificial intelligence and machine learning are
discussed. This thesis also proposes a new video surveillance system architecture introducing fog
computing towards IoT based system and contributes the facilities and benefits of proposed system
which can meet the forthcoming requirements of surveillance. Different challenges and issues
faced for video surveillance in IoT environment and evaluate fog-cloud integrated architecture to
penetrate and eliminate those issues.
The focus of this thesis is to evaluate the IoT based video surveillance system. To this end,
two case studies were performed to penetrate values towards energy and bandwidth efficient video
surveillance system. In one case study, an IoT-based power efficient color frame transmission and
generation algorithm for video surveillance application is presented. The conventional way is to
transmit all R, G and B components of all frames. Using proposed technique, instead of sending
all components, first one color frame is sent followed by a series of gray-scale frames. After a
certain number of gray-scale frames, another color frame is sent followed by the same number of
gray-scale frames. This process is repeated for video surveillance system. In the decoder, color
information is formulated from the color frame and then used to colorize the gray-scale frames. In
another case study, a bandwidth efficient and low complexity frame reproduction technique that is
also applicable in IoT based video surveillance application is presented. Using the second
technique, only the pixel intensity that differs heavily comparing to previous frameâs
corresponding pixel is sent. If the pixel intensity is similar or near similar comparing to the
previous frame, the information is not transferred. With this objective, the bit stream is created for
every frame with a predefined protocol. In cloud side, the frame information can be reproduced by
implementing the reverse protocol from the bit stream.
Experimental results of the two case studies show that the IoT-based proposed approach
gives better results than traditional techniques in terms of both energy efficiency and quality of the video, and therefore, can enable sensor nodes in IoT to perform more operations with energy
constraints
The Future of the Internet
Presents findings from a survey of technology leaders, scholars, industry officials, and analysts. Evaluates the network infrastructure's vulnerability to attack, and the Internet's impact on various institutions and activities in the coming decade
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