Global Image Recognition in Retail 2020-2025, by Technology, Component, application, implementation and impact of COVID-19 –

News Highlights: Global Image Recognition in Retail 2020-2025, by Technology, Component, application, implementation and impact of COVID-19 –

DUBLIN– (BUSINESS WIRE) –The “Global Image Recognition in Retail (2020-2025), by Technology, Component, Application, Implementation, Geography and Impact of Covid-19 with Ansoff Analysis “ report has been added to ResearchAndMarkets.com’s offering.

Global retail image recognition is estimated at $ 1.5 billion by 2020 and is expected to reach $ 3.7 billion by 2025, with a CAGR of 20%.

Image recognition means identifying a specific image and placing it in a predetermined category. It uses computer algorithms for digital image processing to help process videos and remove blurry images. The technology further makes it possible to convert images to two or more defined dimensions, which categorize the digital image processing as multi-dimensional systems.

Retail Image Recognition encompasses the technologies that help improve the shopping experience for customers. The use of high bandwidth data services in the retail and BFSI sector can be attributed to the growth of the image recognition market. android devices with cameras attract sellers to invest in the market. The increasing demand for security in products and applications is also influencing the growth of the image recognition market.

Large companies in various sectors, such as retail, automotive, healthcare and defense, are increasingly adopting image recognition technology. Several other areas, such as self-driving vehicles, automated image organization of visual websites, and facial recognition on social networking websites, use the image recognition technology enabled by machine learning.

Government agencies such as law enforcement agencies also use facial recognition technology for their safety and security purposes. Airports also use facial recognition technology in security checks for security reasons. Recent advancements in artificial intelligence and machine learning have contributed greatly to the growth of image recognition and object detection in the retail industry.

Certain factors can hinder the growth of image recognition in the retail sector, such as the high costs involved in making the image recognition systems, a lack of technical skills, etc. Thus, companies lacking sufficient resources cannot adopt this technology even if they are interested in image recognition.

On the other hand, there are a large number of social media applications available on the internet, and a large part of the population uploads a billion images per day on social media platforms such as Facebook, WhatsApp, Snapchat, Instagram, etc., which will help increase image recognition adoption and drive the growth of global image recognition in retail.

Market dynamics

Drivers

• Need to increase shelf availability, improve customer experience and maximize ROI

• Increase the use of image recognition applications

• Increased use of high bandwidth data services

• Increasing demand for security applications and products with image recognition functions

Limitations

Opportunities

• Increasing adoption of cloud-based image recognition solutions

• Rising demand for brand recognition among end users

• Increasing demand for big data analysis

Main topics:

1. Report description

1.1 Study goals

1.2 Market definition

1.3 Currency

1.4 years considered

1.5 Language

1.6 Major Shareholders

2. Research methodology

2.1 Research Process

2.2 Data collection and validation

2.2.1 Secondary research

2.2.2 Primary research

2.3 Estimation of the market size

2.4 Assumptions of the Study

2.5 Limitations of the Study

3. Summary

4. Market overview

4.1 Introduction

4.2 Market dynamics

4.2.1 Directors

4.2.2 Limitations

4.2.3 Features

4.2.4 Challenges

4.3 Trends

5. Market analysis

5.1 Porter’s Five Forces Analysis

5.2 Impact of COVID-19

5.3 Ansoff matrix analysis

5.4 SWOT analysis

6. Global Image Recognition in Retail, by Technology

6.1 Introduction

6.2 Barcode recognition

6.3 Digital image processing

6.4 Object recognition

6.5 Face recognition

6.6 Other Technologies

7. Worldwide image recognition in retail, per component

7.1 Introduction

7.2 Services

7.3 Software

7.4 Hardware

8. Global image recognition in retail, by application

8.1 Introduction

8.2 Visual search for products

8.3 Marketing and Advertising

8.4 Security and surveillance

8.5 Vision analysis

8.6 Other uses

9. Global retail image recognition, per implementation

9.1 Introduction

9.2 Cloud

9.3 On location

10. Worldwide retail image recognition, by geography

10.1 Introduction

10.2 North America

10.2.1 USA

10.2.2 Canada

10.2.3 Mexico

10.3 South America

10.3.1 Brazil

10.3.2 Argentina

10.4 Europe

10.4.1 UK

10.4.2 France

10.4.3 Germany

10.4.4 Italy

10.4.5 Rest of Europe

10.5 Asia-Pacific

10.5.1 China

10.5.2 Japan

10.5.3 India

10.5.4 Australia

10.5.5 Rest of APAC

10.6 Middle East and Africa

11. Competitive landscape

11.1 Competitive Quadrant

11.2 Market share analysis

11.3 Competition scenario

11.3.1 Mergers and Acquisitions

11.3.2 Agreements, collaborations and partnerships

11.3.3 New Product Launches and Enhancements

11.3.4 Investments and financing

12. Company Profiles

12.1 Amazon

12.2 Google LLC

12.3 IBM Corporation

12.4 Microsoft Corporation

12.5 TRAX Retail

12.6 Qualcomm Technologies, Inc.

12.7 NEC Corporation

12.8 LTU Technologies

12.9 Catchoom Technologies SL

12.10 Honeywell International Inc.

12.11 Hitachi, Ltd.

12.12 Slyce Inc.

12.13 Wikitude GmbH

12.14 Attrasoft, Inc.

12.15 pm Planorama

12.16 Ricoh Innovations Corporation

12.17 Pattern Recognition Company GMBH

12.18 Intelligent retail

12.19 Snap2Insight Inc.

12.20 Blippar

For more information on this report, visit https://www.researchandmarkets.com/r/8b9j61

Via: www.businesswire.com

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