Achieving a Secure and Reliable Business Via Video Analytics - insideBIGDATA

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In this special guest feature, Anusha Jayasundara, a software engineer at WSO2, takes a look at some common use cases for video analytics, and then examines the underlying technology of this growing field. Anusha is a software engineer on the WSO2 real-time analytics team, where he researches and analyzes various methods of video processing to support solutions, such as surveillance and monitoring. The global market for video analytics is expected to reach $11.17 billion by 2022, according to a recent research report by MarketsandMarkets. This growth comes as newer analytics technologies are being applied to a range of applications--automated surveillance and traffic control, to name just two--that allow people to gain greater insights from video data and turn them into action. However, to fully capitalize on video processing, organizations need to leverage the right methodologies for extracting high-level information from video sources such as camera feeds.


VIVOTEK Aims to Become the Veritable 'Eye' in the IoT

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For every country security and surveillance has become a key to survival. With rise in terrorism coupled with challenging security conditions, investment in security and surveillance by India has become a necessity. Coupled with the IoT capabilities, the security and surveillance sector is sure to be the next level thing in the industry. We believe that the best parameter to judge the performance of any business is the success that it has received in the market. Considering this, VIVOTEK has etched its name in gold not only in India, but on an international level as well.


Video meets the Internet of Things

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Video-analytics technology is transforming the Internet of Things and creating new opportunities. Are companies prepared to capture growth? Some of the most innovative Internet of Things (IoT) applications involve video analytics--a technology that applies machine-learning algorithms to video feeds, allowing cameras to recognize people, objects, and situations automatically. These applications are relatively new, but several factors are encouraging their growth, including the increased sophistication of analytical algorithms and lower costs for hardware, software, and storage. With video analytics becoming more important to IoT applications, we decided to examine this technology more closely.


What is Deep Learning? A Complete Guide to AI in Security

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The security industry has experienced its share of rapid changes that have been well documented in recent years. The question is how soon will the next wave of potential disruption really take hold? Since many sensor manufacturers do not have the expertise in these areas, go-to market strategies have been mostly by partnering with video analytics solution providers taking their first steps into AI. For the physical/electronic security industry, AI essentially refers to systems that show intelligent behavior by analyzing their environment they can perform various tasks with some degree of autonomy to achieve specific goals. That concept seems relatively straightforward to grasp, but the deep learning and machine learning aspects driving AI may need more explanation.


The Emerging Potential for Video Analytics-as-a-Service

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Video surveillance is one of the fastest growing segments in the physical security industry. In the prevailing security environment, the need for video surveillance is growing exponentially. From smart cities to stadiums, from retail mega-markets to homes, video surveillance has become a pervasive phenomenon. Several petabytes of video data are being generated globally every year from this growing number of video surveillance installations. However, a large amount of video which is captured is never analyzed for actionable intelligence and, in many cases, a large team of human operators is required to monitor the video feeds.