The processing and analysis of visual data are known as video analytics. Intelligent video analytics or computer vision are terms used to describe the application of artificial intelligence to extract information from data, however, video analytics is still frequently used as a shorthand.
Video analytics shines out as a significant opportunity. It has the potential to be a game-changing application for edge computing due to the following:
- The big and expanding market – in 2021, an estimated one billion surveillance cameras were operational worldwide. The number of cameras expects to increase by 20 percent between 2017 and 2024, therefore, AI and analytics will be more and more crucial to capturing value from the massive amount of video footage collected daily.
- The market expansion potential of edge computing – without edge computing, issues with data sovereignty, and the cost of transferring high-bandwidth data to the cloud impede the use of video analytics (a problem that is heightened as video streams increase in quality). Therefore, edge computing allows cost-effective video analytics, including more advanced AI/ML-enabled analytics.
- Its applicability to nearly all industries – many use cases can be addressed by video analytics, from analyzing football players’ kicking mechanics to comprehending consumer behavior in retail. In the case of video analytics for security, it applies to almost all industries, including manufacturing, education, transportation, and a long list of other fields.
By 2030, video analytics on the edge market will be worth $75 billion. In 2021, a quarter of edge computing sales came from video analytics, a significant application for private 5G and edge computing (topped only by cloud gaming). The global market for edge-enabled video analytics was worth over $5 billion in 2021. At a CAGR of 34 percent, it expects to increase to $75 billion by 2030.
There are many application areas for video analytics, of which three are –
Security and Surveillance
Faced-based touchless access control, intrusion detection, and perimeter protection. Identifying security risks to trigger alerts or automated responses. Examples of security and surveillance customers include retail, transport, manufacturing, government, and more.
Production and Maintenance
Real-time detection of problems during operational processes (manufacturing line). Monitoring of assets to identify wear-and-tear (enables predictive maintenance). Examples of production and maintenance customers are manufacturing, logistics, construction, oil, gas & mining, and more.
Flow Analysis
Creating actionable traffic maps based on how people move. It is used for real-time crowd control or footfall analysis in planning and optimization. Examples of flow analysis industry customers are retail, transport, and government.
There will be more opportunities for video analytics in production and maintenance by 2030. Along with the transition to Industry 4.0 and a rise in automation that leads to an increase in sensors and analytics, this will grow over the course of the next ten years. Therefore, the use case will see significant adoption in the manufacturing, oil & gas, and logistics verticals.
