The world of computer vision has evolved rapidly in recent years, and computers are now more accurate than humans in some application areas. Computer vision has enabled many applications that were only part of science fiction a short time ago. The rise of artificial intelligence (AI) has been instrumental in reaching the level of accuracy that the market demands. Meanwhile, a new application of computer vision called video analytics (VA) has emerged. VA deals with the extraction of data from incoming video and the generation of results that are meaningful to humans. The need for security and safety has been the primary driver of VA solutions, and many companies offer products pertaining to the security needs of businesses. However, business intelligence, where the data generated by VA is used for business purposes, has emerged as another strong use case driven by the retail market. The intelligence has been used for marketing, operations analysis, and other business purposes. Over the past several years, VA has proven to be an ideal solution that meets the needs of surveillance system operators, security officers, and corporate managers. Maturing computer vision algorithms, coupled with increasing computational capacity and increased camera resolution, have helped increase the accuracy of results. Many industries have  adopted VA systems in different application areas that extend well beyond security. Omdia forecasts that global VA revenue will increase from $1.1 billion in 2018 to $4.5 billion in 2025. This Omdia report examines the market trends surrounding VA technologies and presents forecasts through 2025. It presents an in-depth analysis of the significant market drivers, market barriers, application markets, and technology issues, in addition to detailed profiles of key industry players. The major application markets covered include government, retail, consumer, smart city, critical infrastructure, and other. Market forecasts are segmented by world region, application market, use case (security vs. business intelligence), and intelligence location (edge vs. on-premises/cloud).

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