Video Analytics

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Video Analytics, also referred to as Video Content Analysis (VCA), is a generic term used to describe computerized processing and analysis of video streams. Computer analysis of video is currently implemented in a variety of fields and industries; however the term "Video Analytics" is typically associated with analysis of video streams captured by surveillance systems. Video Analytics applications can perform a variety of tasks ranging from real-time analysis of video for immediate detection of events of interest, to analysis of pre-recorded video for the purpose of extracting events and data from the recorded video (also known as forensic analysis). Video analytics use computer processing power to analyze the differences between one video image and the next. Pixels that are different between the two images being compared are grouped into objects.


How Video Analytics at the Edge is Transforming Security and Surveillance Lanner

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Video surveillance and security plays a huge part in our everyday lives and is becoming both increasingly widespread and intelligent thanks in large part to the integration of analytics and edge computing that has emerged from the expansion of the Internet of Things (IoT) and the Fourth Industrial Revolution. While the IoT has expanded into a significant percentage of industrial and commercial sectors, surveillance and security, especially video surveillance, is currently being radically transformed by the convergence of multiple technologies including video surveillance systems, connected IoT devices, edge computing and artificial intelligence and machine learning. Through this transformation, video analytics has evolved into an essential technology for those employing video surveillance and security systems and the IoT, alongside maturing edge computing systems, is currently helping to develop even more intelligent video analytics solutions. As well as the previously mentioned technologies, artificial intelligence and machine learning algorithms are also helping to make video analytics solutions much more capable. In this article, we'll be looking at how the introduction of edge computing into video analytics has begun to transform the security and surveillance efforts of businesses and organisations all over the world.


Sipping from the Video Firehose: Energy's Use of Real-Time Analytics - UrIoTNews

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A generation of video applications treats cameras more like the internet of things (IoT) devices by monitoring asset conditions, identifying equipment using bar codes, license plates, or the movement of vehicles and personnel. Video cameras are ubiquitous in the energy sector and have been for many years. Being asset-intensive industries, they have a lot of very expensive equipment and operations that may pose hazards to protect. As a result, perimeter security and general surveillance are critical applications for video in energy. However, applications are emerging that use cameras for completely different tasks โ€“ creating a new and expanding role for video and, especially, analytics.


50 Examples of Video Analytics Applications (Infographic) Lanner

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Artificial Intelligence and advancements in cloud edge computing infrastructure are enabling development of intelligent real-time video analytics solutions that are solving several problems and creating many opportunities in different sectors. By harnessing the capabilities of machine learning and bid data, AI-powered video analytics solutions have started to play a crucial role in automating several functions and duties based on video intelligence collected through application specific cameras. From street crime deterrence, to missing person search, patient monitoring, land surveying, vehicle classification, product fault detection and wildlife poaching control, there are numerous applications of video analytics solutions. In below linked infographic we will show you 50 of those applications across different sectors.


Unassisted AI Video Surveillance Techniques Help Numerous Verticals to Scale โ€“ ReadWrite

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Data has always been a business game changer as a rear view indicator. It's been defined as the new oil. Most of the time, data gets collected, stored, and then analyzed to find the right insights through multiple sets of tools. With this cumbersome approach, reaching those critical data points requires considerable time. In the process, opportunities are lost and greater costs are accumulated.