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How Computer Vision Is Changing Retail

Welcome to the fifth installment of Voxel51’s computer vision industry spotlight blog series. Each edition, we highlight how different industries – from construction to climate tech, from medicine to robotics, and more – are using computer vision, machine learning, and artificial intelligence to drive innovation. We’ll dive deep into the main computer vision tasks being put to use, current and future challenges, and companies at the forefront.

In this edition, we’ll focus on retail! Read on to learn about computer vision in the retail and ecommerce industry.

Retail Industry Overview

Retail is an important pillar of modern economies, bridging the gap between producers and consumers. It’s a sector that facilitates commerce and reflects and shapes societal trends and consumer preferences.

Key facts and figures:

Applying computer vision and AI to retail opens up a whole new world of possibilities. Recent innovations in both retail and retail technologies have set the stage for today’s advancements. Thanks to vision- and AI-powered technologies, retailers can get a better read on what shoppers want, meet those needs more sustainably, and continue making shopping an enjoyable omnichannel affair.

Before we dive into various popular applications of computer vision-based AI technologies in retail, it’s important to highlight the key challenges facing the industry.

Key Industry Challenges in Retail

Continue reading to learn about several ways in which computer vision applications are helping organizations in the retail industry.

Computer Vision Applications in Retail

Inventory Management & Supply Chain Optimization

Sample output of a computer vision algorithm used to detect and identify products on the shelf. Image source: Curvelogics

Inventory management is all about getting the right products to customers at the right place and time, avoiding frustrating stockouts and wasteful overstocking. Computer vision is an ideal ingredient in inventory management systems due to the availability of visual data from cameras and other sensors that make it possible to monitor and analyze inventory levels in real time. 

Computer vision techniques at the core of AI-powered inventory management systems include object detection, object recognition, image classification, and more. Visual search is increasing in popularity, enabling consumers to search for products using images instead of (or in addition to) text.

Vision-based inventory management systems bring a variety of benefits to retailers and consumers, including enhanced operational efficiency, increased customer satisfaction, cost savings, and streamlined supply chains. The automation of inventory processes also frees up valuable staff time, allowing workforces to focus on other value-added tasks and continuing to improve the overall shopping experience for customers.

Computer vision is also paving the way for a new suite of AI-powered tools to help retailers optimize inventory management, including smart shelf solutions, AI route optimization for deliveries, store layout optimization, and in-store shopper analytics.

For further reading on the use of computer vision and AI technologies for automated inventory management at popular retailers, visit the following resources:

Additionally, here are a few academic papers related to using computer vision for real-time inventory management:

Autonomous Checkouts & Smart Carts

People using self-checkout systems in cashierless Amazon Go stores. Image source: Forbes

Autonomous checkout systems and smart carts in retail utilize computer vision to deliver a fast, efficient shopping experience. By employing cameras, scanners, sensors, and object recognition concepts, these systems can instantly recognize and tally products. Shoppers can simply place their items in a designated area, and the system automatically calculates the total, facilitating a seamless and rapid checkout experience.

For retailers, self-checkout systems increase checkout speeds, accuracy, and efficiency, while also reducing labor costs, which is especially important in a tight labor market. For shoppers, contactless checkout systems offer a smooth grab-and-go shopping experience, while reducing the time spent at checkout counters and enhancing overall convenience.

Visit these resources for further reading on automated checkout systems at popular retailers:

Check out these papers about using computer vision for automated checkout systems:

Virtual Try-Ons

Sample output of virtual try-on by Warby Parker

Virtual try-ons in retail allow customers to virtually “wear” clothing, accessories, and makeup from the comfort of their own homes using digital overlays on their images or live feeds. These systems analyze the user’s physical features using pose estimation, image segmentation, and 3D modeling, and superimpose products on them, providing a realistic virtual representation of how the items would look worn in real life.

Using virtual try-ons makes shopping a breeze and fun, letting folks visualize how the products would look on them without having to try things on in real life. Virtual try-on technologies reduce the number of returns, increase online engagement, and offer a competitive edge in the ecommerce landscape, as well as create unique and compelling reasons for consumers to make in-person visits to stores.

Check out these resources on virtual try-on technologies at popular retailers:

Here are a few papers related to using computer vision for virtual try-ons:

Customer Behavior Analysis

Sample output of computer vision based customer shopper path analytics. Image source: Quora

Retail stores can leverage existing CCTV cameras to analyze customer behavior. Based on video footage from these cameras, object tracking algorithms can track customer movements, dwell times, and interactions within the store. This provides insights into shopping patterns, popular areas, and product preferences.

The benefits of understanding customer behavior are substantial. For retailers, it offers actionable insights to optimize store layouts, enhance product placements, and tailor marketing strategies. It also aids in predicting shopping trends, allowing for better inventory management. It results in a more personalized shopping experience, as stores can adjust their offerings and layouts based on observed preferences.

Here are a few papers related to using computer vision for customer behavior analytics:

Product Recommendations

Multimodal search (image and text) on Amazon. Image source: Amazon via TechCrunch

Computer vision enhances product recommendation systems, opening up new possibilities for customer engagement and personalization.

For example, visual search adds a convenient way for consumers to discover new products and information, beyond text searches alone. A growing number of retailers, including IKEA and Amazon through its multimodal (image and text) search, offer the ability for consumers to search for a product by uploading an image.

Recommender systems can present items based on visual similarity to uploaded images, as well additional factors such as recent browsing history, wishlist items, and past purchases, to tailor the shopping experience to an individual consumer’s style and preferences.

Check out these academic papers on computer vision in recommender systems:

Companies at the Cutting Edge of Computer Vision in Retail


Using computer vision to detect items picked up by shoppers. Image source: Trigo

Trigo combines ceiling-based cameras, shelf sensors, and machine vision algorithms to create a digital twin of the retail space. This digital representation allows for real-time analysis of shoppers’ journeys and product choices, enabling better shopping experiences and business outcomes. This setup also enables computer-vision-based autonomous checkout systems that accurately identify and capture the shopping items selected by customers, and make checking out entirely automated. Trigo Vision has attracted significant investments, notably from the German retail giant REWE Group, pushing Trigo’s total fundraising to over $100 million.

Trax Retail

After gathering, analyzing, and reporting data, Trax uses the insights and its artificial-intelligence tech to improve operations and increase sales in brick-and-mortar stores. Image source: Trax

Trax Retail’s mission is to enable brands and retailers to harness the power of digital technologies to produce the best shopping experiences imaginable. Trax’s retail platform allows customers to understand what is happening on-shelf, in every store, all the time so they can focus on what they do best – delighting shoppers. As pioneers in computer vision, Trax continues to lead the industry in innovation and excellence through development of advanced technologies and scalable data collection methods.

Many of the world’s top CPG companies and retailers use Trax’s dynamic merchandising, in-store execution, shopper engagement, market measurement, analytics, and shelf monitoring solutions at scale to drive positive shopper experiences and unlock revenue opportunities at all points of sale.

Link Retail

Computer vision being used for people counting. Image source: Link Retail

Link Retail, based in Oslo, Norway, uses AI powered techniques to help brick-and-mortar retailers strategically boost sales and optimize operations. The company builds a variety of solutions, including:

  • Food Waste Management: AI software that optimizes grocery product ordering procedures and reduces retail food waste
  • Video Analytics: A high accuracy footfall counting system that turns in-store CCTV camera footage into rich operational and shopping data, such as real-time occupancy analysis, shopper flow, and queue analytics
  • Space Management: An AI analytics tool that employs Point of Sales (POS) data and generates actionable insights on optimizing retail space including floor, shelf, and sales activities

Link Retail helps retailers navigate the challenges of the physical retail environment, making strides toward creating data-driven retail spaces.

Dayta AI

Computer vision used for customer analytics in retail. Image source: Dayta AI

Dayta AI is a retail analytics Software as a Service (SaaS) company that uses computer vision and AI to turn camera footage from retail stores into useful insights. Dayta AI’s solution, Cyclops, is engineered to work with any RTSP-supported cameras, allowing retailers to use their existing video cameras without additional equipment costs. Cyclops can monitor and analyze customer traffic, zone-specific activities, footfall, engagement count, dwell time, queue time, and even emotions, among other metrics. These data points help retailers understand customer behavior, optimize store layouts, and improve the overall customer experience.


RetailNext’s Traffic 2.0 for accurate foot traffic measurement. Image source: RetailNext

RetailNext was founded to address challenges faced by modern retailers and bring e-commerce style shopper analytics to brick-and-mortar stores, brands, and malls. Through its centralized SaaS platform, RetailNext automatically collects and analyzes shopper behavior data, providing retailers with the critical insights they need to improve the shopper experience in real time. This platform helps retailers optimize store operations, store layouts and marketing strategies, and improve customer experiences​​. The company also offers a next-generation IoT sensor, Aurora, which is powered by an patented algorithm that uses 3D imagery and deep learning. RetailNext’s technology is trusted by 400+ top retailers and brands globally.

Retail Datasets

If you are interested in exploring applications of computer vision in retail, check out these datasets:

If you would like to see any of these or other computer vision retail datasets added to the FiftyOne Dataset Zoo, get in touch, and we can work together to make this happen!

Join the FiftyOne Community!

Developers of retail applications can benefit from FiftyOne’s ability to easily filter through the huge amounts of visual data collected daily from stores and other sources. Using open source FiftyOne, this data can be curated into datasets for model training or to share with experts for annotation or analysis of CV models.

Join the thousands of engineers and data scientists already using FiftyOne to solve some of the most challenging problems in computer vision today!