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.
Given the increasing affordability of equipment and growing awareness of security requirements, more and more cameras are being installed across the globe every day. While this is a good thing, the sheer volume of footages that come in makes it difficult for operators to find specific objects or people when needed. This is one area where artificial intelligence (AI) is all set to play a key role. Several security companies are already working on this. Make searching through videos as simple as using Google.
In a city of the future, it would be nice to know quickly if there's a fire burning out of control, a crime in progress at a certain location, or a traffic snarl at a particular corner. Nvidia hopes to detect such problems in smart cities using Nvidia Metropolis, which the company said could pave the way for the creation of smart artificial intelligence cities. Nvidia announced the tech ahead of its GPU Technology conference this week in San Jose, California. Metropolis is a video analytics platform that applies deep learning AI to video streams for applications such as public safety, traffic management, and resource optimization. Nvidia said that Metropolis could make cities safer, and more than 50 partner companies are already providing products and applications for AI city uses based on graphics processing units (GPUs) made by Nvidia. "Deep learning is enabling powerful intelligent video analytics that turn anonymized video into real-time valuable insights, enhancing safety and improving lives," said Deepu Talla, vice president and general manager of the Tegra business at Nvidia, in a statement.
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.
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.