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Computer vision in retail: use cases,
examples & implementation guidelines
February 19, 2026
Computer vision in retail implies using AI systems that analyze visual data from retail environments to help companies derive business insights and automate workflows. The technology is being widely adopted to streamline various back-office operations and enable convenient shopping experiences.
Typically implemented in brick-and-mortar stores for use cases like video surveillance, retail software solutions powered by computer vision are also proving invaluable in ecommerce, offering capabilities such as visual search and virtual try-on to shorten the path to purchase both in brick-and-mortar and online stores.
$12.6
bn
the expected value of the global computer vision in retail market by 2033
Grand View Research
$19
bn
in abandoned purchases that can be prevented with computer vision-based cashierless systems
DHL
Computer vision use cases & real-world examples in retail
Inventory management automation
Computer vision has a lot to offer when it comes to streamlining inventory management operations, with automated stock level monitoring being the top use case. Instead of manually scanning each product in the sales floor and backroom, retailers can use AI-powered cameras, fixed or mounted on robots, and image recognition software to identify and count products on shelves, detect empty spaces, and alert staff about the need for restocking. Computer vision solutions can also help retailers ensure planogram compliance in the store, verifying whether products are correctly displayed in the right aisle. Furthermore, these solutions can identify damaged packages to help ensure product integrity, as well as verify that shelf price tags match the respective products and the prices recorded in the POS system.
In 2024, UK supermarket chain Morrisons adopted a similar solution provided by Focal Systems to improve stock availability and staff efficiency. AI-based cameras constantly monitor shelves to identify out-of-stock or low-stock products, restocked items, and cases of planogram non-compliance, helping employees quickly replenish empty shelves and focus on customer service.
Retail store layout optimization
Along with its process automation potential, computer vision technology can also generate insights for strategic retail design decisions by monitoring customer behavior within stores. For instance, video analytics software can track foot traffic, identify popular routes or sections that are often ignored, and provide store layout recommendations for optimal product placement, such as complementary items displayed together to foster buying decisions. These systems can also generate heat maps to highlight high-density aisles or areas with longer dwell times, including promotional displays and advertisements, helping retailers assess their effectiveness based on customer engagement.
One example is the American energy company Phillips 66 that implemented an AWS-based computer vision system across its gas stations' convenience stores to derive actionable insights from point-of-sale footage. The solution, named Connected Store, provides multiple benefits, from identifying optimal promotional spaces based on customer behavior to alerting staff when stocks are running low.
Automated checkout
In an effort to minimize wait times at checkout lines, many retail companies offer self-checkout options, which enable customers to manually scan and pay for items at a kiosk. Cashierless stores represent a step forward, with fully automated checkout systems that leverage computer vision to track customers picking products off the shelf, recognize each item, and add it to the customer's virtual shopping cart. These solutions typically identify shoppers via an app or card swipe upon entry and automatically charge their accounts as they leave the store.
Convenience store chain Amazon Go pioneered this grab-and-go approach with its “Just Walk Out” technology. Amazon's initiative proved especially effective when implemented in small retail environments, such as corner stores, and the company is now focusing on implementing it across third-party stores in NFL stadiums, airports, and colleges.
Virtual try-on
Virtual try-on solutions rely on a combination of computer vision and augmented reality, allowing customers to digitally try on clothes, cosmetics, and other types of products via in-store smart mirrors or their mobile devices. These systems analyze camera feeds to map key facial and other body features, such as eye shape for makeup or build for shirts, track the user's movements in real time, and precisely overlay a 3D model of the selected product onto a live image of the customer.
One the world's largest cosmetics companies, L'Oréal is also a leading provider of virtual try-on technology, with its AI and AR division ModiFace licensing virtual try-on solutions to other major beauty brands such as Maybelline and Lancome. Modiface uses deep learning to track and render face, hair, and nails, so that the visualization can automatically adapt to different skin tones and lighting conditions for maximum accuracy.
Image title: L'Oréal’s ModiFace rendering feature
Image source: ModiFace
Visual search & contextual commerce
Visual search is a computer vision-based technology that enables consumers to search for products in ecommerce websites’ catalogs using images instead of text keywords. Visitors can upload the photo of a product, either stored on their device or captured live with a camera, to a retailer's website. The built-in AI engine analyzes this picture to identify key features, including shape, color, and texture, providing users with a selection of visually similar products. Amazon, Target, IKEA, and other major retailers have already incorporated this functionality in their online stores and platforms.
Another way of routing customers to the products they need involves integrating contextual commerce functionality into social media platforms. In this case, a computer vision system identifies products within uploaded image and video content, searches for similar items across partners’ online catalogs, and enables users to reach the corresponding product pages via embedded links. A prime example of a company reaping the benefits of these solutions is AiBUY, a US leader in shoppable media technology. The organization partnered with Itransition to develop a video commerce platform featuring computer vision-powered product recognition capabilities for contextual commerce.
Image title: A fashion show video with an overlay linking to mentioned products
Video surveillance
Computer vision technology is helping retail businesses ensure safer shopping environments without having to manually monitor stores 24/7. Along with more traditional object detection tasks, such as identifying intruders during closing hours, modern computer vision solutions can recognize complex behavioral patterns to detect shoplifting in real time. This involves detecting suspicious customer movements across store aisles, such as concealing items or unusually lingering near high-value products, to trigger an alert. Retailers can also deploy these artificial intelligence systems at self-service checkouts to detect unintentional or deliberate mis-scanning, typically by cross-checking the video feed with POS data.
Major retail chains, such as Tesco and Lidl, have recently implemented this type of solution, sometimes referred to as “supermarket VAR (video assistant referee)”, to address the growing number of shoplifting offences. However, thanks to the increasing accessibility of this technology, even small convenience stores can now adopt computer vision systems with relative ease.
Image title: Computer vision-powered theft detection solution by Veesion
Image
source: Veesion
Image title: Tesco's mis-scanning detection system
Image source: Daily Mail
Enhance your retail operations with a computer vision solution by Itransition
Payoffs of adopting computer vision in the retail industry
Improved on-shelf availability
Ensuring shelves are always stocked and thus preventing lost sales due to out-of-stocks thanks to automated inventory management.
Increased staffing efficiency
Freeing up employees from time-consuming store operations and boosting their operational efficiency through automated shelf monitoring and cashierless checkout solutions.
Superior conversion rates
Driving sales conversions both in physical stores through optimized store layout and on ecommerce websites via virtual search and contextual commerce functionality.
Faster checkout
Eliminating or significantly reducing checkout wait times for more convenient shopping and maximum customer satisfaction by automating the checkout process.
Lower return rates
Reducing product returns by allowing customers to better evaluate fit and appearance during online shopping through virtual try-on technology.
Enhanced loss prevention
Minimizing retail shrinkage by detecting suspicious behavior to prevent theft through automated video surveillance systems.
How to address computer vision adoption challenges in retail
Concerns | Recommendations | |
|---|---|---|
Environmental constraints |
Computer vision systems often operate in suboptimal conditions due to the dynamism and unpredictability of
retail environments. For instance, poor lighting and frequent occlusion by customers or other products can
reduce object recognition accuracy.
| Rely on sensor fusion, namely a combination of multiple computer vision-powered cameras, weight sensing devices, and other sensors to better track customer-product interactions and overcome any potential environmental constraints. |
IT infrastructure limitations |
Retailers can use legacy systems, such as POS or inventory management software, which are difficult to
integrate with modern computer vision solutions due to outdated communication protocols. Additionally,
companies can lack sufficient bandwidth and computing resources to transmit and process visual data in real
time.
|
Set up an enterprise service bus (ESB) serving as a middleware to convert different communication protocols
and solve potential compatibility issues between existing systems and the new AI solution. |
Privacy concerns |
In many jurisdictions, video recording is governed by strict privacy laws, such as the GDPR in the EU. If a
computer vision system tracks customers' features and movements without their explicit consent, it risks
violating regulations on biometric data collection.
|
Use anonymization techniques, including real-time blurring on facial traits and other identifiers, at the
camera or edge level before data is even stored or transmitted. |
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Maximizing the value of computer vision in retail
Over the past decade, the role of computer vision in retail has grown steadily thanks to advances in machine learning technology, which have opened new avenues for improving the customer experience and streamlining key retail operations. Modern computer vision solutions powered by ML algorithms can recognize customers’ complex behavioral patterns to interpret their intent, as well as accurately identify products even in non-ideal operating scenarios.
Businesses can further amplify computer vision’s potential by combining this technology with other AI-powered tools, such as retail predictive analytics. For instance, visual data like foot traffic can complement historical sales data, enabling retailers to forecast future sales trends with superior accuracy and optimize their supply chain operations accordingly.
To unlock and fully leverage these capabilities, build powerful computer vision solutions tailored to your needs with an experienced IT partner like Itransition.
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