Artificial Intelligence in Computer Vision Market by Component, Technology, Function, Application, Deployment Mode, End-Use Industry

Description

List of Tables

The Artificial Intelligence in Computer Vision Market was valued at USD 26.11 billion in 2023 and is projected to grow to USD 32.12 billion in 2024, with a CAGR of 24.02%, reaching USD 117.89 billion by 2030.

KEY MARKET STATISTICS
Base Year [2023]	USD 26.11 billion
Estimated Year [2024]	USD 32.12 billion
Forecast Year [2030]	USD 117.89 billion
CAGR (%)	24.02%

Artificial Intelligence has emerged as a fundamental driver of innovation in computer vision, transforming traditional imaging and perception techniques into sophisticated, automated processes. In recent years, AI-powered computer vision solutions have revolutionized how industries capture, analyze, and act upon visual data. This transformation is not only reshaping product development and service delivery but also establishing new operational benchmarks for accuracy and efficiency.

Advancements in deep learning, machine learning, and real-time processing have allowed systems to learn from vast datasets, resulting in improved image recognition, object tracking, and contextual understanding. As companies harness the power of these evolving technologies, they remain better equipped to address complex challenges such as autonomous navigation, quality inspection, and surveillance. The integration of AI in computer vision is propelling industries to rethink strategies, optimize workflows, and anticipate future market trends through innovative applications.

The goal of this overview is to provide a clear and comprehensive perspective on how AI has reinvigorated the realm of computer vision, driving transformative change and opening up new avenues for business growth. The rapid pace of technological shifts promises not only enhanced efficiency but also the creation of entirely new market segments and opportunities, ensuring that enterprises continue to stay ahead in a competitive global landscape.

Transformative Shifts: How Technological Advancements Are Redefining the Computer Vision Landscape

Recent technological evolutions have led to profound changes in the computer vision landscape. A strategic convergence of cutting-edge neuroscience, advanced sensor technologies, and increased computational power has catalyzed further innovations in image processing and recognition. The capacity to integrate AI-driven features with traditional computer vision models has enabled businesses to streamline processes in manufacturing, healthcare, automotive, and retail sectors.

Seamless integration between hardware and software components is now standard, and many organizations are transitioning to more agile, adaptive models that leverage real-time data and cloud-based deployments. This alignment of high-resolution sensors, dedicated AI algorithms, and adaptive middleware has redefined how digital visual data is captured and analyzed. The impact is resonating across multiple industries, from autonomous vehicles to security monitoring, where faster, more accurate decision-making is essential.

As firms pivot to innovative models that emphasize speed and scalability, the fusion of deep learning and computer vision is driving enhancements that were once thought to be the exclusive domain of science fiction. Companies are now positioned to not only improve day-to-day operations but also to unlock latent market opportunities by harnessing these transformative shifts.

Key Segmentation Insights: Illuminating Diverse Perspectives in a Multifaceted Market

The computer vision market has been meticulously segmented to capture the nuanced interplay of technology, application, and industry requirements. When examining segmentation based on components, the market is analyzed across hardware, services, and software. In this context, hardware encompasses detailed studies of cameras and sensors, while the services category explores consulting and training to empower adopters of new technologies. Software is dissected further into AI algorithms and middleware, which are vital in translating raw data into actionable insights.

Furthermore, segmentation based on technology offers a layered perspective that spans from 3D computer vision-with additional focus on stereo vision and structured light-to deep learning, which is systematically divided into convolutional neural networks and recursive neural networks. The narrative continues with machine learning segmented in terms of supervised and unsupervised learning, complemented by natural language processing, where speech recognition and text analysis contribute to broader contextual understanding.

Analyzing functional segmentation reveals how processes such as identification, localization, reconstruction, and tracking are optimized. For example, identification covers both human and object detection, while localization extends to indoor and outdoor mapping scenarios. The segmentation journey continues with reconstruction, focusing on environment and surface details, followed by tracking, where behavioral and motion patterns are monitored. Additionally, segmentation by application includes facets such as 3D modeling, gesture recognition, image recognition, and machine vision, while deployment modes are examined in cloud-based versus on-premises setups. Finally, segmentation by end-use industry spans automotive, healthcare, manufacturing, retail, and security and surveillance, each with its own unique demands and technological prerequisites.

Based on Component, market is studied across Hardware, Services, and Software. The Hardware is further studied across Cameras and Sensors. The Services is further studied across Consulting and Training. The Software is further studied across AI Algorithms and Middleware.

Based on Technology, market is studied across 3D Computer Vision, Deep Learning, Machine Learning, and Natural Language Processing. The 3D Computer Vision is further studied across Stereo Vision and Structured Light. The Deep Learning is further studied across Convolutional Neural Networks and Recursive Neural Networks. The Machine Learning is further studied across Supervised Learning and Unsupervised Learning. The Natural Language Processing is further studied across Speech Recognition and Text Analysis.

Based on Function, market is studied across Identification, Localization, Reconstruction, and Tracking. The Identification is further studied across Human Identification and Object Identification. The Localization is further studied across Indoor Mapping and Outdoor Mapping. The Reconstruction is further studied across Environment Reconstruction and Surface Reconstruction. The Tracking is further studied across Behavior Tracking and Motion Tracking.

Based on Application, market is studied across 3D Modeling, Gesture Recognition, Image Recognition, and Machine Vision.

Based on Deployment Mode, market is studied across Cloud-Based and On-Premises.

Based on End-Use Industry, market is studied across Automotive, Healthcare, Manufacturing, Retail, and Security & Surveillance.

Key Regional Insights: Navigating Market Dynamics Across Diverse Global Regions

The geographical spread of the computer vision market accentuates the varying dynamics and opportunities in different regions. In the Americas, robust investments in technology and infrastructure are propelling advancements, driving a surge in the adoption of AI-driven visual systems across diverse industrial sectors.

In the Europe, Middle East & Africa region, historical innovation combined with a focus on regulatory frameworks and sustainable growth is paving the way for tailored implementations of computer vision solutions. The region is rapidly evolving with collaborative initiatives that integrate academic research with practical, real-world applications.

Meanwhile, the Asia-Pacific region stands out for its rapid digitization imperatives and expansive production ecosystems, which serve as both a crucible for innovation and a significant market in their own right. As these regions continue to contribute distinctively to the market landscape, they collectively shape a vibrant, multifaceted market that is geared to address both local and global technological challenges.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Key Companies Insights: Leaders Driving the Future of Computer Vision

A wide spectrum of companies is steering the advancement of computer vision across various domains by investing in research and pushing the boundaries of technological innovation. Dominant players such as Adobe Inc. and Advanced Micro Devices, Inc. have leveraged their engineering capabilities to integrate powerful visual computing capabilities into mainstream applications. Giants like Amazon Web Services, Inc. and Apple Inc. have redefined infrastructure and consumer experience by employing scalable models and state-of-the-art devices.

Innovative entities like Arm Limited and Basler AG have established a strong foundation in hardware performance, while Clarifai, Inc. and Cognex Corporation are at the forefront of developing breakthrough imaging software and analytical solutions. Similarly, global conglomerates such as Fujitsu Limited, Google LLC by Alphabet Inc., and Hailo Technologies Ltd. continue to push the innovation envelope through strategic investments and collaborative ventures. Huawei Technologies Co., Ltd. and Infosys Limited have also made significant strides in integrating cutting-edge computer vision tools within business ecosystems.

Continued contributions from Intel Corporation, International Business Machines Corporation, Landing AI, and LXT AI Inc. underscore the collaborative efforts toward streamlining operational efficiencies. With further advancements from Meta Platforms, Inc., Microsoft Corporation, and NetApp, Inc., the competitive landscape is constantly evolving. Noteworthy progress by Nvidia Corporation, Oracle Corporation, Qualcomm Technologies, Inc., and Raydiant Inc. complements a dynamic environment that is further enriched by initiatives from Samsung Electronics Co. Ltd., TechSee Augmented Vision Ltd., Unity Software Inc., and Wovenware, Inc. Finally, strategic interventions from Maxar Technologies Inc. and XenonStack Pvt. Ltd. highlight the sector's focus on innovative, data-driven approaches.

The report delves into recent significant developments in the Artificial Intelligence in Computer Vision Market, highlighting leading vendors and their innovative profiles. These include Adobe Inc., Advanced Micro Devices, Inc., Amazon Web Services, Inc., Apple Inc., Arm Limited, Basler AG, Clarifai, Inc., Cognex Corporation, Fujitsu Limited, Google LLC by Alphabet Inc., Hailo Technologies Ltd., Huawei Technologies Co., Ltd., Infosys Limited, Intel Corporation, International Business Machines Corporation, Landing AI, LXT AI Inc., Meta Platforms, Inc., Microsoft Corporation, NetApp, Inc., Nvidia Corporation, Oracle Corporation, Qualcomm Technologies, Inc., Raydiant Inc., Samsung Electronics Co. Ltd., TechSee Augmented Vision Ltd., Unity Software Inc., Wovenware, Inc. by Maxar Technologies Inc., and XenonStack Pvt. Ltd.. Actionable Recommendations: Strategic Directions for Industry Leaders in Computer Vision

To sustain competitive advantage in an increasingly complex technological ecosystem, leaders must adopt a multi-dimensional strategy. It is essential to prioritize investments in research and development while embracing a flexible approach that allows for rapid adaptation to field innovations. Emphasizing a balance between in-house development and strategic partnerships can accelerate product innovation and reduce time-to-market gaps.

Leaders are encouraged to actively leverage emerging trends such as deep learning and advanced sensor technologies, while also focusing on strengthening the hardware-software ecosystem. By closely monitoring regional market dynamics and regional differences, organizations can tailor solutions that address localized needs. Moreover, investing in cloud-based deployment models will enhance scalability and resilience, ensuring sustained performance even in rapidly evolving industry environments.

Another critical recommendation is the integration of comprehensive training initiatives and effective knowledge transfer programs to upskill workforce across various technical domains, ultimately leading to higher productivity and innovation momentum.

Conclusion: Embracing Change and Harnessing Innovation in the Computer Vision Space

In summary, artificial intelligence has ushered in a new era in computer vision, marked by significant advancements that enhance precision, efficiency, and operational effectiveness. The multifaceted segmentation analysis highlights the intricacies of component design, technological evolution, functional deployments, application areas, and industry-specific requirements. Each segment reinforces the transformative nature of AI-driven computer vision technologies, offering unparalleled opportunities for growth and competitive differentiation.

The insights derived from regional studies and prominent corporate contributions further underscore the necessity for organizations to adapt swiftly to the emerging trends. The convergence of diverse technological components coupled with dynamic market strategies is paving the way for robust performance improvements across multiple industries. As challenges persist, forward-thinking leaders will continue to drive meaningful change by embracing innovative solutions that not only resolve current operational inefficiencies but also set the foundation for sustained future success.

Product Code: MRR-432EC3BC8E7C

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Proliferating shift of automotive industry toward autonomous vehicles
  - 5.1.1.2. Rise in demand for automation across manufacturing and retail sector for quality control operations
- 5.1.2. Restraints
  - 5.1.2.1. Complexities in integration with other sensors and systems
- 5.1.3. Opportunities
  - 5.1.3.1. Integrating AI computer vision in healthcare for patient monitoring
  - 5.1.3.2. Rising investment and funding activities in emerging startups
- 5.1.4. Challenges
  - 5.1.4.1. Ethical and privacy concerns associate with computer vision technologies
5.2. Market Segmentation Analysis
- 5.2.1. Component: Increasing consumer preference for AI-powered computer vision solution
- 5.2.2. Technology: Expanding usage of 3D Computer vision technologies in autonomous vehicles
- 5.2.3. Function: Growing need for facial recognition technologies in law enforcement and access control systems
- 5.2.4. Application: Increasing use of AI in computer vision for facial detection and object detection for unique identification
- 5.2.5. Deployment Modes: Expansion of cloud deployment of AI in computer vision owing to better scalability and cost efficiency
- 5.2.6. End-Use Industry: Increasing adoption of AI computer vision in manufacturing and retail businesses to reduce the workload complexity
5.3. Porter's Five Forces Analysis
- 5.3.1. Threat of New Entrants
- 5.3.2. Threat of Substitutes
- 5.3.3. Bargaining Power of Customers
- 5.3.4. Bargaining Power of Suppliers
- 5.3.5. Industry Rivalry
5.4. PESTLE Analysis
- 5.4.1. Political
- 5.4.2. Economic
- 5.4.3. Social
- 5.4.4. Technological
- 5.4.5. Legal
- 5.4.6. Environmental

6. Artificial Intelligence in Computer Vision Market, by Component

6.1. Introduction
6.2. Hardware
- 6.2.1. Cameras
- 6.2.2. Sensors
6.3. Services
- 6.3.1. Consulting
- 6.3.2. Training
6.4. Software
- 6.4.1. AI Algorithms
- 6.4.2. Middleware

7. Artificial Intelligence in Computer Vision Market, by Technology

7.1. Introduction
7.2. 3D Computer Vision
- 7.2.1. Stereo Vision
- 7.2.2. Structured Light
7.3. Deep Learning
- 7.3.1. Convolutional Neural Networks
- 7.3.2. Recursive Neural Networks
7.4. Machine Learning
- 7.4.1. Supervised Learning
- 7.4.2. Unsupervised Learning
7.5. Natural Language Processing
- 7.5.1. Speech Recognition
- 7.5.2. Text Analysis

8. Artificial Intelligence in Computer Vision Market, by Function

8.1. Introduction
8.2. Identification
- 8.2.1. Human Identification
- 8.2.2. Object Identification
8.3. Localization
- 8.3.1. Indoor Mapping
- 8.3.2. Outdoor Mapping
8.4. Reconstruction
- 8.4.1. Environment Reconstruction
- 8.4.2. Surface Reconstruction
8.5. Tracking
- 8.5.1. Behavior Tracking
- 8.5.2. Motion Tracking

9. Artificial Intelligence in Computer Vision Market, by Application

9.1. Introduction
9.2. 3D Modeling
9.3. Gesture Recognition
9.4. Image Recognition
9.5. Machine Vision

10. Artificial Intelligence in Computer Vision Market, by Deployment Mode

10.1. Introduction
10.2. Cloud-Based
10.3. On-Premises

11. Artificial Intelligence in Computer Vision Market, by End-Use Industry

11.1. Introduction
11.2. Automotive
11.3. Healthcare
11.4. Manufacturing
11.5. Retail
11.6. Security & Surveillance

12. Americas Artificial Intelligence in Computer Vision Market

12.1. Introduction
12.2. Argentina
12.3. Brazil
12.4. Canada
12.5. Mexico
12.6. United States

13. Asia-Pacific Artificial Intelligence in Computer Vision Market

13.1. Introduction
13.2. Australia
13.3. China
13.4. India
13.5. Indonesia
13.6. Japan
13.7. Malaysia
13.8. Philippines
13.9. Singapore
13.10. South Korea
13.11. Taiwan
13.12. Thailand
13.13. Vietnam

14. Europe, Middle East & Africa Artificial Intelligence in Computer Vision Market

14.1. Introduction
14.2. Denmark
14.3. Egypt
14.4. Finland
14.5. France
14.6. Germany
14.7. Israel
14.8. Italy
14.9. Netherlands
14.10. Nigeria
14.11. Norway
14.12. Poland
14.13. Qatar
14.14. Russia
14.15. Saudi Arabia
14.16. South Africa
14.17. Spain
14.18. Sweden
14.19. Switzerland
14.20. Turkey
14.21. United Arab Emirates
14.22. United Kingdom

15. Competitive Landscape

15.1. Market Share Analysis, 2023
15.2. FPNV Positioning Matrix, 2023
15.3. Competitive Scenario Analysis
- 15.3.1. EyePop.ai transforms access to computer vision technology with the launch of self-service AI platform
- 15.3.2. Air India launches AI-based computer vision technology to enhance passenger experience and operational efficiency
- 15.3.3. Caregility introduces first edge-based computer vision AI capability for healthcare
- 15.3.4. Axelera AI raises USD 68 million to enhance AI hardware, targeting efficiency and market expansion in computer vision advancements
- 15.3.5. AI-driven autonomous inspection software transforms energy asset management with cloud-based computer vision solutions
- 15.3.6. Assert AI secures USD 4 million to transform AI-powered video analytics and expand global footprint
- 15.3.7. Fiji Airways partners with Assaia to leverage its AI-powered computer vision technology to elevate operational efficiency
- 15.3.8. Syntax systems partners with cogniac to propel industrial AI with enterprise-grade vision
- 15.3.9. France-based AI-powered computer vision startup XXII raises EUR 22 million funding
- 15.3.10. Assert AI launches Pratham to democratizing computer vision and artificial intelligence
- 15.3.11. Lenskart acquires Tango Eye to elevate retail experience and streamline manufacturing processes
- 15.3.12. Landing AI launches App Space to enable rapid vision system development
- 15.3.13. Landing AI and Snowflake partner to empower AI-driven computer vision on unstructured data
- 15.3.14. Pangiam and West Virginia University Research Corporation partner to develop artificial intelligence and computer vision technology
- 15.3.15. Meta unveils DINOv2, an AI vision transfer model
- 15.3.16. Intel Labs advances computer vision development with two new AI models
- 15.3.17. Deep Learning Robotics receives patent for revolutionary computer vision technology
- 15.3.18. KPM Analytics acquires food focused AI developer Smart Vision Works
- 15.3.19. Healthy.io awarded three new U.S. patents for wound-image technology
- 15.3.20. Phantom AI secures USD 36.5 million to advance driver-assistance systems, enhancing road safety through affordable and innovative AI solutions in the expanding ADAS market
- 15.3.21. Plainsight and Connection Launch AI Alliance to Bring Computer Vision to Enterprises Across Industries
- 15.3.22. Michelin Acquires Computer Vision Start-Up RoadBotics
15.4. Strategy Analysis & Recommendation
- 15.4.1. Landing AI
- 15.4.2. Nvidia Corporation
- 15.4.3. Meta Platforms, Inc.
- 15.4.4. Intel Corporation

Companies Mentioned

1. Adobe Inc.
2. Advanced Micro Devices, Inc.
3. Amazon Web Services, Inc.
4. Apple Inc.
5. Arm Limited
6. Basler AG
7. Clarifai, Inc.
8. Cognex Corporation
9. Fujitsu Limited
10. Google LLC by Alphabet Inc.
11. Hailo Technologies Ltd.
12. Huawei Technologies Co., Ltd.
13. Infosys Limited
14. Intel Corporation
15. International Business Machines Corporation
16. Landing AI
17. LXT AI Inc.
18. Meta Platforms, Inc.
19. Microsoft Corporation
20. NetApp, Inc.
21. Nvidia Corporation
22. Oracle Corporation
23. Qualcomm Technologies, Inc.
24. Raydiant Inc.
25. Samsung Electronics Co. Ltd.
26. TechSee Augmented Vision Ltd.
27. Unity Software Inc.
28. Wovenware, Inc. by Maxar Technologies Inc.
29. XenonStack Pvt. Ltd.