Crowd Analytics Market by Component (Services, Solutions), Deployment Mode (Cloud, Hybrid, On-Premises), Enterprise Size, Organization Type, Analytics Type, Use Case, End User Industry - Global Forecast 2025-2032
Description
The Crowd Analytics Market was valued at USD 1.69 billion in 2024 and is projected to grow to USD 2.05 billion in 2025, with a CAGR of 21.53%, reaching USD 8.06 billion by 2032.
A concise orientation to how advancements in sensing and analytics are redefining operational resilience and stakeholder experience across crowded spaces
The growing complexity of public gatherings, transportation hubs, retail environments, and institutional venues has elevated the strategic importance of crowd analytics for organizations seeking to optimize safety, operational efficiency, and customer experience. Advances in sensing, computer vision, and cloud-native architectures are converging to enable richer behavioral insights at scale, transforming how facilities are managed and services are delivered. In parallel, rising expectations around privacy and regulatory compliance are reshaping technology selection and deployment strategies, requiring multidisciplinary approaches that balance capability with governance.
Decision-makers now operate at the intersection of real-time operational imperatives and longer-term resilience planning. The maturation of thermal analytics, video analytics, and hybrid deployment models supports both strategic and tactical use cases ranging from queue management to emergency response. As organizations move from pilot projects to wider rollouts, integration with existing IT and OT systems becomes a defining challenge and opportunity, necessitating clear roadmaps for data interoperability, edge-processing architectures, and vendor governance. Ultimately, the introduction contextualizes crowd analytics not just as a point technology but as an embedded capability that underpins smarter, safer, and more responsive environments.
How breakthroughs in edge compute, ML model governance, and privacy-first design are reshaping procurement priorities and operational models across organizations
The landscape for crowd analytics is undergoing transformative shifts driven by technological innovation, changing regulatory expectations, and evolving operational priorities. Advances in machine learning models and edge compute capabilities are enabling higher-fidelity detections with lower latency, which in turn permits actionable automation at the point of service or intervention. This shift from human-in-the-loop to hybrid automated operations is changing the mix of required skills, with greater emphasis on data engineering, model governance, and systems integration rather than pure algorithm design.
Concurrently, the industry has seen an increased demand for privacy-preserving architectures and explainable analytics as stakeholders seek defensible processes for data collection and retention. These expectations are catalyzing new product design patterns that embed anonymization, on-device processing, and rigorous access controls. In operational terms, the move toward cloud and hybrid deployments is enabling centralized analytics and cross-site benchmarking, while edge deployments preserve responsiveness and privacy where needed. As a result, procurement and deployment decisions are now informed by broader enterprise imperatives-resilience, compliance, and interoperability-rather than purely feature-driven comparisons, signaling a more strategic, ecosystem-oriented phase of market evolution.
Assessing the cumulative effects of recent U.S. tariff adjustments on procurement flexibility, supplier strategies, and vendor roadmaps for analytics deployments
Policy shifts and tariff actions in the United States for 2025 have introduced additional variables into procurement planning, supply chain resilience strategies, and capital allocation for analytics hardware and integrated solutions. Organizations that rely on cross-border hardware sourcing, specialized sensors, or third-party system integrations now face a landscape where import costs and supplier lead times can materially affect deployment timelines and total cost of ownership. In response, procurement teams are reevaluating supplier diversity, engaging in forward stocking where feasible, and considering longer-term service agreements to hedge against volatility.
These dynamics are also influencing product roadmaps, with vendors accelerating software-led differentiation that reduces sensitivity to hardware cost fluctuations by offering modular, device-agnostic solutions and subscription-based analytic packages. Meanwhile, system integrators and managed service providers are increasingly packaging installation, maintenance, and analytics as bundled services to smooth price variability for end users. From a strategic perspective, the cumulative policy impact is prompting organizations to prioritize flexibility in procurement clauses, to incorporate contingency planning into project timelines, and to strengthen vendor assurance measures such as multi-sourcing and localized support capabilities.
Deep segmentation-driven perspectives that explain how industry, component choices, deployment modes, enterprise characteristics, and use cases determine solution fit and adoption trajectories
Insights drawn from segmentation reveal where value is being created and where deployment strategies must adapt. Based on End User Industry, market dynamics differ notably across Bfsi, Healthcare, Retail, and Transportation, with each vertical prioritizing distinct outcome metrics such as risk mitigation, patient flow optimization, shopper experience, and transit throughput. These industry-specific priorities shape feature requirements, integration points, and governance expectations, requiring vendors to tailor both the messaging and technical architecture of their offerings.
Based on Component, market choices oscillate between Services and Solutions. Services are further differentiated into Managed Services and Professional Services, which address ongoing operations and bespoke integration needs respectively. Solutions bifurcate into Hardware and Software, with Software further categorized into Thermal Analytics and Video Analytics, driving a product mix that blends sensing fidelity with analytical depth. Based on Deployment Mode, organizations evaluate Cloud, Hybrid, and On-Premises options to balance latency, privacy, and centralized analytics needs, resulting in deployment blueprints that reflect operational constraints.
Based on Enterprise Size, requirements diverge between Large Enterprises and Small And Medium Enterprises, where scale, budget cadence, and internal IT capacity determine adoption pathways. Based on Organization Type, Private and Public entities impose different procurement frameworks and regulatory obligations that affect timelines and documentation. Based on Analytics Type, choices between Batch and Real Time analytics influence infrastructure design and alerting models. Finally, based on Use Case, priorities such as Behavior Analysis, Crowd Counting, Heat Mapping, and Queue Management create discrete value streams that inform commercial pricing, deployment sequencing, and success criteria for projects.
Comparative regional landscape analysis that highlights distinct regulatory, infrastructure, and buyer behavior patterns across the Americas, EMEA, and Asia-Pacific
Regional analysis exposes differentiated adoption patterns and operational priorities that influence go-to-market approaches and partnership strategies. In the Americas, demand is frequently driven by large-scale retail operations, major transit authorities, and financial institutions focused on regulatory compliance and customer experience; buyers here often prioritize integrations with existing enterprise systems and centralized analytics workflows. Europe, Middle East & Africa presents a mosaic of regulatory regimes and infrastructure maturity levels, where privacy regulation and local procurement rules shape deployment models and drive preference for on-premises or hybrid configurations in sensitive environments.
Asia-Pacific demonstrates rapid uptake across urbanized environments and transportation networks, with a high appetite for scalable cloud-native solutions and smart city integrations. Vendors operating across regions should therefore design flexible commercial models and localized support capabilities to match diverse procurement cycles, compliance frameworks, and infrastructure profiles. Cross-regional strategies that combine global platform consistency with regionalized deployment and support practices increase the likelihood of successful expansion while mitigating operational friction caused by regulatory divergence and logistical constraints.
How vendor differentiation through privacy-first design, modular architectures, and service-centric commercial models is shaping competitive advantage and partnership strategies
Competitive dynamics are characterized by a mix of specialized analytics providers, large-scale systems integrators, and niche hardware manufacturers who collectively define solution breadth and depth. Leading companies are differentiating through investments in privacy-preserving analytics, edge-optimized inference engines, and service models that lower integration friction for enterprise IT teams. Partnerships between analytics vendors and domain specialists-such as transportation operators, healthcare systems, and retail chains-are increasingly important for co-developing validated use cases and accelerating deployment timelines.
In this competitive environment, companies that prioritize modular architectures, open APIs, and rigorous interoperability testing gain traction with procurement teams seeking to avoid vendor lock-in. Similarly, firms that offer robust managed services and outcome-based commercial models can reduce buyer friction and accelerate the shift from pilot to production. Across the vendor landscape, those that demonstrate strong governance frameworks, transparent data-handling practices, and demonstrable case studies for operational impact are positioned to win multi-site, cross-functional engagements.
Practical, enterprise-grade recommendations for aligning procurement, privacy governance, hybrid architecture, and organizational readiness to maximize operational value
Industry leaders should adopt a pragmatic, outcomes-focused approach that prioritizes interoperability, governance, and skill development. First, align procurement with enterprise architecture by specifying open APIs, data schema standards, and clear integration points to reduce custom engineering costs and accelerate deployment. Second, embed privacy and compliance requirements into product selection criteria by demanding explainable models, on-device anonymization options, and retention controls that meet prevailing regulatory standards. These measures not only reduce legal exposure but also increase stakeholder trust and long-term program viability.
Third, favor hybrid implementation roadmaps that allow for edge responsiveness in mission-critical contexts and centralized analytics for trend analysis and benchmarking; this balanced architecture supports both immediate operational needs and strategic insights. Fourth, invest in organizational readiness through role-based training, cross-functional governance committees, and vendor performance KPIs to ensure sustained value capture. Finally, consider outcome-linked commercial arrangements and bundled managed services to transfer operational risk and simplify total cost assessment for executive sponsors. Collectively, these recommendations help leaders convert technical capability into measurable operational improvements while managing risk.
A transparent research methodology combining practitioner interviews, technical validation, and document analysis to deliver robust, verifiable insights without relying on proprietary market estimates
This analysis synthesizes primary and secondary research sources, structured interviews with industry practitioners, technical validation of vendor offerings, and comparative evaluation of deployment case studies. Primary engagement included dialogues with solution architects, procurement leads, and operational managers across multiple industries to capture real-world constraints and success factors. Secondary analysis encompassed a systematic review of white papers, regulatory guidance, and product documentation to corroborate feature sets and architectural approaches without relying on proprietary market estimates or forecasts.
The research approach emphasizes methodological rigor through triangulation: qualitative insights from interviews were validated against documented product capabilities and anonymized deployment case studies to ensure factual accuracy. Technical evaluations focused on latency profiles, model explainability features, and data governance mechanisms, while procurement narratives highlighted contract structures, support SLAs, and integration complexities. Limitations and assumptions have been explicitly recorded to aid interpretation, and the methodology supports replication and targeted follow-up research to address specific organizational questions or geographic nuances.
Strategic conclusions that stress modularity, governance, and pragmatic deployment roadmaps as the foundation for scalable and resilient analytics programs
In summary, crowd analytics has entered a phase where technological maturity, privacy expectations, and operational urgency intersect to create durable demand for integrated, governance-aware solutions. Organizations that treat analytics as an enterprise capability rather than a point solution will unlock greater value by aligning procurement with architecture, investing in cross-functional readiness, and mandating interoperable, privacy-first designs. Regional and industry nuances require tailored approaches, but common success factors include modular systems, managed service pathways, and demonstrable governance frameworks.
Leaders who proactively address supplier diversification, hybrid deployment strategies, and outcome-linked commercial models will be better positioned to navigate geopolitical and supply-side uncertainty while accelerating time to operational impact. The path forward favors pragmatism: prioritize interoperable solutions, require demonstrable data protections, and design phased implementations that deliver early wins while establishing the foundation for scale. This balanced approach reduces risk, increases stakeholder confidence, and enables measurable improvements in safety, throughput, and user experience across crowded environments.
Note: PDF & Excel + Online Access - 1 Year
A concise orientation to how advancements in sensing and analytics are redefining operational resilience and stakeholder experience across crowded spaces
The growing complexity of public gatherings, transportation hubs, retail environments, and institutional venues has elevated the strategic importance of crowd analytics for organizations seeking to optimize safety, operational efficiency, and customer experience. Advances in sensing, computer vision, and cloud-native architectures are converging to enable richer behavioral insights at scale, transforming how facilities are managed and services are delivered. In parallel, rising expectations around privacy and regulatory compliance are reshaping technology selection and deployment strategies, requiring multidisciplinary approaches that balance capability with governance.
Decision-makers now operate at the intersection of real-time operational imperatives and longer-term resilience planning. The maturation of thermal analytics, video analytics, and hybrid deployment models supports both strategic and tactical use cases ranging from queue management to emergency response. As organizations move from pilot projects to wider rollouts, integration with existing IT and OT systems becomes a defining challenge and opportunity, necessitating clear roadmaps for data interoperability, edge-processing architectures, and vendor governance. Ultimately, the introduction contextualizes crowd analytics not just as a point technology but as an embedded capability that underpins smarter, safer, and more responsive environments.
How breakthroughs in edge compute, ML model governance, and privacy-first design are reshaping procurement priorities and operational models across organizations
The landscape for crowd analytics is undergoing transformative shifts driven by technological innovation, changing regulatory expectations, and evolving operational priorities. Advances in machine learning models and edge compute capabilities are enabling higher-fidelity detections with lower latency, which in turn permits actionable automation at the point of service or intervention. This shift from human-in-the-loop to hybrid automated operations is changing the mix of required skills, with greater emphasis on data engineering, model governance, and systems integration rather than pure algorithm design.
Concurrently, the industry has seen an increased demand for privacy-preserving architectures and explainable analytics as stakeholders seek defensible processes for data collection and retention. These expectations are catalyzing new product design patterns that embed anonymization, on-device processing, and rigorous access controls. In operational terms, the move toward cloud and hybrid deployments is enabling centralized analytics and cross-site benchmarking, while edge deployments preserve responsiveness and privacy where needed. As a result, procurement and deployment decisions are now informed by broader enterprise imperatives-resilience, compliance, and interoperability-rather than purely feature-driven comparisons, signaling a more strategic, ecosystem-oriented phase of market evolution.
Assessing the cumulative effects of recent U.S. tariff adjustments on procurement flexibility, supplier strategies, and vendor roadmaps for analytics deployments
Policy shifts and tariff actions in the United States for 2025 have introduced additional variables into procurement planning, supply chain resilience strategies, and capital allocation for analytics hardware and integrated solutions. Organizations that rely on cross-border hardware sourcing, specialized sensors, or third-party system integrations now face a landscape where import costs and supplier lead times can materially affect deployment timelines and total cost of ownership. In response, procurement teams are reevaluating supplier diversity, engaging in forward stocking where feasible, and considering longer-term service agreements to hedge against volatility.
These dynamics are also influencing product roadmaps, with vendors accelerating software-led differentiation that reduces sensitivity to hardware cost fluctuations by offering modular, device-agnostic solutions and subscription-based analytic packages. Meanwhile, system integrators and managed service providers are increasingly packaging installation, maintenance, and analytics as bundled services to smooth price variability for end users. From a strategic perspective, the cumulative policy impact is prompting organizations to prioritize flexibility in procurement clauses, to incorporate contingency planning into project timelines, and to strengthen vendor assurance measures such as multi-sourcing and localized support capabilities.
Deep segmentation-driven perspectives that explain how industry, component choices, deployment modes, enterprise characteristics, and use cases determine solution fit and adoption trajectories
Insights drawn from segmentation reveal where value is being created and where deployment strategies must adapt. Based on End User Industry, market dynamics differ notably across Bfsi, Healthcare, Retail, and Transportation, with each vertical prioritizing distinct outcome metrics such as risk mitigation, patient flow optimization, shopper experience, and transit throughput. These industry-specific priorities shape feature requirements, integration points, and governance expectations, requiring vendors to tailor both the messaging and technical architecture of their offerings.
Based on Component, market choices oscillate between Services and Solutions. Services are further differentiated into Managed Services and Professional Services, which address ongoing operations and bespoke integration needs respectively. Solutions bifurcate into Hardware and Software, with Software further categorized into Thermal Analytics and Video Analytics, driving a product mix that blends sensing fidelity with analytical depth. Based on Deployment Mode, organizations evaluate Cloud, Hybrid, and On-Premises options to balance latency, privacy, and centralized analytics needs, resulting in deployment blueprints that reflect operational constraints.
Based on Enterprise Size, requirements diverge between Large Enterprises and Small And Medium Enterprises, where scale, budget cadence, and internal IT capacity determine adoption pathways. Based on Organization Type, Private and Public entities impose different procurement frameworks and regulatory obligations that affect timelines and documentation. Based on Analytics Type, choices between Batch and Real Time analytics influence infrastructure design and alerting models. Finally, based on Use Case, priorities such as Behavior Analysis, Crowd Counting, Heat Mapping, and Queue Management create discrete value streams that inform commercial pricing, deployment sequencing, and success criteria for projects.
Comparative regional landscape analysis that highlights distinct regulatory, infrastructure, and buyer behavior patterns across the Americas, EMEA, and Asia-Pacific
Regional analysis exposes differentiated adoption patterns and operational priorities that influence go-to-market approaches and partnership strategies. In the Americas, demand is frequently driven by large-scale retail operations, major transit authorities, and financial institutions focused on regulatory compliance and customer experience; buyers here often prioritize integrations with existing enterprise systems and centralized analytics workflows. Europe, Middle East & Africa presents a mosaic of regulatory regimes and infrastructure maturity levels, where privacy regulation and local procurement rules shape deployment models and drive preference for on-premises or hybrid configurations in sensitive environments.
Asia-Pacific demonstrates rapid uptake across urbanized environments and transportation networks, with a high appetite for scalable cloud-native solutions and smart city integrations. Vendors operating across regions should therefore design flexible commercial models and localized support capabilities to match diverse procurement cycles, compliance frameworks, and infrastructure profiles. Cross-regional strategies that combine global platform consistency with regionalized deployment and support practices increase the likelihood of successful expansion while mitigating operational friction caused by regulatory divergence and logistical constraints.
How vendor differentiation through privacy-first design, modular architectures, and service-centric commercial models is shaping competitive advantage and partnership strategies
Competitive dynamics are characterized by a mix of specialized analytics providers, large-scale systems integrators, and niche hardware manufacturers who collectively define solution breadth and depth. Leading companies are differentiating through investments in privacy-preserving analytics, edge-optimized inference engines, and service models that lower integration friction for enterprise IT teams. Partnerships between analytics vendors and domain specialists-such as transportation operators, healthcare systems, and retail chains-are increasingly important for co-developing validated use cases and accelerating deployment timelines.
In this competitive environment, companies that prioritize modular architectures, open APIs, and rigorous interoperability testing gain traction with procurement teams seeking to avoid vendor lock-in. Similarly, firms that offer robust managed services and outcome-based commercial models can reduce buyer friction and accelerate the shift from pilot to production. Across the vendor landscape, those that demonstrate strong governance frameworks, transparent data-handling practices, and demonstrable case studies for operational impact are positioned to win multi-site, cross-functional engagements.
Practical, enterprise-grade recommendations for aligning procurement, privacy governance, hybrid architecture, and organizational readiness to maximize operational value
Industry leaders should adopt a pragmatic, outcomes-focused approach that prioritizes interoperability, governance, and skill development. First, align procurement with enterprise architecture by specifying open APIs, data schema standards, and clear integration points to reduce custom engineering costs and accelerate deployment. Second, embed privacy and compliance requirements into product selection criteria by demanding explainable models, on-device anonymization options, and retention controls that meet prevailing regulatory standards. These measures not only reduce legal exposure but also increase stakeholder trust and long-term program viability.
Third, favor hybrid implementation roadmaps that allow for edge responsiveness in mission-critical contexts and centralized analytics for trend analysis and benchmarking; this balanced architecture supports both immediate operational needs and strategic insights. Fourth, invest in organizational readiness through role-based training, cross-functional governance committees, and vendor performance KPIs to ensure sustained value capture. Finally, consider outcome-linked commercial arrangements and bundled managed services to transfer operational risk and simplify total cost assessment for executive sponsors. Collectively, these recommendations help leaders convert technical capability into measurable operational improvements while managing risk.
A transparent research methodology combining practitioner interviews, technical validation, and document analysis to deliver robust, verifiable insights without relying on proprietary market estimates
This analysis synthesizes primary and secondary research sources, structured interviews with industry practitioners, technical validation of vendor offerings, and comparative evaluation of deployment case studies. Primary engagement included dialogues with solution architects, procurement leads, and operational managers across multiple industries to capture real-world constraints and success factors. Secondary analysis encompassed a systematic review of white papers, regulatory guidance, and product documentation to corroborate feature sets and architectural approaches without relying on proprietary market estimates or forecasts.
The research approach emphasizes methodological rigor through triangulation: qualitative insights from interviews were validated against documented product capabilities and anonymized deployment case studies to ensure factual accuracy. Technical evaluations focused on latency profiles, model explainability features, and data governance mechanisms, while procurement narratives highlighted contract structures, support SLAs, and integration complexities. Limitations and assumptions have been explicitly recorded to aid interpretation, and the methodology supports replication and targeted follow-up research to address specific organizational questions or geographic nuances.
Strategic conclusions that stress modularity, governance, and pragmatic deployment roadmaps as the foundation for scalable and resilient analytics programs
In summary, crowd analytics has entered a phase where technological maturity, privacy expectations, and operational urgency intersect to create durable demand for integrated, governance-aware solutions. Organizations that treat analytics as an enterprise capability rather than a point solution will unlock greater value by aligning procurement with architecture, investing in cross-functional readiness, and mandating interoperable, privacy-first designs. Regional and industry nuances require tailored approaches, but common success factors include modular systems, managed service pathways, and demonstrable governance frameworks.
Leaders who proactively address supplier diversification, hybrid deployment strategies, and outcome-linked commercial models will be better positioned to navigate geopolitical and supply-side uncertainty while accelerating time to operational impact. The path forward favors pragmatism: prioritize interoperable solutions, require demonstrable data protections, and design phased implementations that deliver early wins while establishing the foundation for scale. This balanced approach reduces risk, increases stakeholder confidence, and enables measurable improvements in safety, throughput, and user experience across crowded environments.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
182 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Implementation of AI-driven real-time crowd density prediction in stadiums and transport hubs
- 5.2. Integration of facial recognition and thermal imaging systems for crowd safety monitoring at mass gatherings
- 5.3. Leveraging anonymized Wi-Fi and Bluetooth signals to model pedestrian flow and dwell time in smart cities
- 5.4. Deployment of edge computing architectures to analyze crowd behavior on-site for faster incident response
- 5.5. Adoption of privacy-preserving federated learning frameworks for cross-venue crowd analytics without sharing raw data
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Crowd Analytics Market, by Component
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.2. Solutions
- 8.2.1. Hardware
- 8.2.2. Software
- 8.2.2.1. Thermal Analytics
- 8.2.2.2. Video Analytics
- 9. Crowd Analytics Market, by Deployment Mode
- 9.1. Cloud
- 9.2. Hybrid
- 9.3. On-Premises
- 10. Crowd Analytics Market, by Enterprise Size
- 10.1. Large Enterprises
- 10.2. Small And Medium Enterprises
- 11. Crowd Analytics Market, by Organization Type
- 11.1. Private
- 11.2. Public
- 12. Crowd Analytics Market, by Analytics Type
- 12.1. Batch
- 12.2. Real Time
- 13. Crowd Analytics Market, by Use Case
- 13.1. Behavior Analysis
- 13.2. Crowd Counting
- 13.3. Heat Mapping
- 13.4. Queue Management
- 14. Crowd Analytics Market, by End User Industry
- 14.1. Bfsi
- 14.2. Healthcare
- 14.3. Retail
- 14.4. Transportation
- 15. Crowd Analytics Market, by Region
- 15.1. Americas
- 15.1.1. North America
- 15.1.2. Latin America
- 15.2. Europe, Middle East & Africa
- 15.2.1. Europe
- 15.2.2. Middle East
- 15.2.3. Africa
- 15.3. Asia-Pacific
- 16. Crowd Analytics Market, by Group
- 16.1. ASEAN
- 16.2. GCC
- 16.3. European Union
- 16.4. BRICS
- 16.5. G7
- 16.6. NATO
- 17. Crowd Analytics Market, by Country
- 17.1. United States
- 17.2. Canada
- 17.3. Mexico
- 17.4. Brazil
- 17.5. United Kingdom
- 17.6. Germany
- 17.7. France
- 17.8. Russia
- 17.9. Italy
- 17.10. Spain
- 17.11. China
- 17.12. India
- 17.13. Japan
- 17.14. Australia
- 17.15. South Korea
- 18. Competitive Landscape
- 18.1. Market Share Analysis, 2024
- 18.2. FPNV Positioning Matrix, 2024
- 18.3. Competitive Analysis
- 18.3.1. AGT International GmbH
- 18.3.2. AllGoVision Technologies Pvt. Ltd.
- 18.3.3. Axis Communications AB
- 18.3.4. Capgemini SE
- 18.3.5. Crowd Dynamics International Limited
- 18.3.6. Crowdanalytix, Inc.
- 18.3.7. CrowdVision Limited
- 18.3.8. Geodan B.V.
- 18.3.9. Graymatics, Inc.
- 18.3.10. Honeywell International Inc.
- 18.3.11. International Business Machines Corporation
- 18.3.12. iOmniscient Pty Ltd
- 18.3.13. Microsoft Corporation
- 18.3.14. NEC Corporation
- 18.3.15. Nokia Corporation
- 18.3.16. Sightcorp B.V.
- 18.3.17. Staqu Technologies Private Limited
- 18.3.18. Ubimo Ltd.
- 18.3.19. Uncanny Vision Solutions Pvt. Ltd.
- 18.3.20. Videonetics Technology Pvt. Ltd.
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