Artificial intelligence is changing video analytics computer vision and machine vision systems, but will it unify disparate and incompatible systems, adding more benefits, asks a new ARC Advisory Group report.
Larry O’Brien, ARC Advisory Group
Machine vision and AI insights
- Learn how artificial intelligence (AI)-powered video analytics has potential for unifying disparate video analytics, computer vision and machine vision systems. An April2025 ARC Strategies report “AI Powered Video Analytics: The Path to a Unified Solution,” from ARC Advisory Group explains more.
- Understand how use of AI for computer vision and machine vision benefit from generative AI, multi-modal foundation models.
- Explore other topics in “AI Powered Video Analytics: The Path to a Unified Solution” from ARC Advisory Group.
Will artificial intelligence (AI)-powered video analytics create a unified approach to disparate video analytics, computer vision and machine vision systems? An April 2025 ARC Strategies report, “AI Powered Video Analytics: The Path to a Unified Solution” from ARC Advisory Group, explains more: “AI is changing the world of video analytics and computer vision. Everyone from machine vision suppliers to platform independent software providers are offering new solutions for a wide range of applications, but can we ever expect a unified approach to AI powered video analytics that can address all computer vision systems?”
The 20-page report explains that computer vision heavily relies on machine learning (ML) techniques to analyze visual data, recognize objects, track movements and more. Industrial AI – the term coined by ARC in 2023 to encompass the full range of AI/ML techniques needed to address diverse and demanding use cases for AI – provides a new foundation for things like image recognition and pattern recognition in industrial applications. It’s being deployed in a wide range of applications for computer vision, from CCTV surveillance to employee safety, condition monitoring and environmental monitoring applications. Computer-vision functions, however, remains largely in silos. The markets for machine vision, CCTV and video surveillance, access control and robotic inspection systems, for example, remain largely compartmentalized. A more unified approach to computer vision can greatly enhance plant safety and reliability, decrease operating costs and automate additional functions.
“AI-enabled computer vision can also substantially increase equipment reliability and availability by improving data-gathering capabilities with advanced sensors and technologies to help gather a wide range of data, uncover information not visible to the eye and flag potential issues that a human may miss. This report looks at the current environment for computer vision applications, addresses the impact of AI and analytics and looks at the potential value and use cases for a unified approach to AI-enabled computer vision across applications and the intersection of technology digital transformation, and sustainable practices,” according to the executive report.
Use of AI for computer vision, machine vision
Computer vision techniques have long been part of the AI landscape. It’s one of the tools in the Industrial AI toolbox benefiting from the massive injection in AI funding triggered initially be the breakthroughs in Gen AI (ChatGPT 3.5 Nov 2022), especially as investments expand beyond large language models (LLMs) into multi-modal foundation models.
Computer vision is experiencing significant advancements thanks to investments in generative AI (gen AI) and the expansion into multi-modal foundation models.
Generative AI in computer vision
Generative AI, particularly techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs), is revolutionizing computer vision by enabling the creation of new, realistic data. This is especially useful for data augmentation, which helps improve the quality and diversity of training datasets.
Some key gen AI benefits for machine and computer include:
- Image-to-image translation: GANs can translate images from one domain to another, such as converting black-and-white images to color or transforming photos into artistic styles.
- Super-resolution: Enhancing the resolution of images without losing detail, which is valuable for medical imaging, satellite imagery and security footage.
- Style transfer: Applying the style of one image to another, useful for artistic expression and marketing.
Multi-modal foundation models integrate vision and language
Multi-modal foundation models, like Magma in February 2025 from Microsoft Research, are designed to handle multiple types of data inputs, such as text, images and videos, simultaneously. These models integrate vision and language understanding, enabling more comprehensive and context-aware analysis. Some benefits of multi-modal foundation models include:
- Enhanced contextual understanding: By combining visual and textual data, these models can provide more accurate and nuanced interpretations of visual information.
- Improved interaction: Multi-modal models can generate more intuitive and interactive user interfaces, transforming how we interact with software and devices.
- Advanced applications: These models are capable of performing complex tasks such as UI navigation, robotic manipulation and real-time decision-making in dynamic environments.
Impacts of AI and machine vision, five segments
The integration of gen AI and multi-modal foundation models in computer vision is leading to more robust, accurate and versatile solutions. These advancements are enhancing existing applications and opening new possibilities in fields like healthcare, security, entertainment and industrial automation.
Today, the market for computer vision and associated analytics capabilities can be split into five major segments: machine vision systems, hyperscalers, CCTV and video surveillance, robotics and drone inspection systems, and platform independent software providers that are not tied to any type of computer-vision technology. In addition to these markets, there is a wide range of open foundations and consortia dedicated machine vision and AI, with many open-source toolsets available.
More about “AI Powered Video Analytics: The Path to a Unified Solution”
The April 2025 ARC Strategies report “AI Powered Video Analytics: The Path to a Unified Solution,” from ARC Advisory Group, includes more on the market for AI-enabled computer vision and video analytics, machine vision providers, video analytics, CCTV and video surveillance, robotics and drone inspection providers, independent platform providers and open foundations along with recommendations for moving toward unified AI-powered video analytics.
Larry O’Brien is vice president of research, ARC Advisory Group. [https://www.arcweb.com/]
Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, [email protected].
KEYWORDS
Machine vision, computer vision, Industrial AI
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