Computer vision in retail: sharper focus, enhanced customer experiences, accelerated business growth
What’s inside
See retail like never before. With computer vision in retail, you gain a clear view of your operations — streamlining inventory, better understanding customer behavior, and driving smarter decisions.
Our expertise in computer vision enables us to deliver innovative, tailored solutions that keep your business ahead of the curve. Trust us to bring clarity, precision, and engineering peace of mind to your retail strategy every step of the way.
Top applications of computer vision in the retail industry
Computer vision in retail is a surefire way to optimize sales-floor and back-office operations, boost customer experience, and enhance security.
Inventory management
- Product availability to prevent out-of-stocks
- Real-time stock level with alerts for replenishment
- Adherence to planograms
- Availability and correctness of price tags
- Expired and soon-to-expire items for timely removal and discounts
- Damaged packaging or compromised product integrity
- Automated inventory counts
- Demand forecasts based on shelf movement patterns
Frictionless checkout
- Cashier-less checkout systems and take-and-go shopping experiences
- Real-time queue management to reduce wait times
- Smart shopping carts that track purchases, calculate totals, and eliminate lines
- Mobile self-checkout with item scanning and payment integration
Personalized customer journeys
- Facial recognition for KYC
- Dynamic in-store offers pushed to customer apps based on their location or preferences
- Targeted marketing based on demographic data and shopping behavior
Interactive shopping experience
- Virtual try-on
- Interactive in-store displays and kiosks
- Visual search (for ecommerce)
Safety and security
- Theft detection through behavior analysis
- Fraud prevention at POS terminals
- Emergency alerts for overcrowded areas
Optimization on the shop floor
- Heatmaps for store layout and product placement optimization
- Stock replenishment optimization
Promotions management
- Monitoring the state of promo zones to ensure products are stocked, priced correctly, and comply with planograms
Optimization of back-office processes
- Automated quality and quantity checks during product acceptance
- Real-time tracking of inventory levels at the store warehouse
Store equipment maintenance (e.g., shelves, refrigeration units)
- Monitoring cleanliness and maintenance requirements
- Detecting equipment malfunctions
Dynamic employee scheduling
- AI-driven allocation of employees based on real-time store traffic
- Optimizing cleaning and restocking tasks during off-peak hours
- Predicting staffing needs for promotions or peak periods
- Identifying areas where additional support is required based on customer activity
- Reducing employee idle time through task prioritization
Sustainability monitoring
- Energy efficiency tracking for refrigeration, lighting, and other systems
- Real-time monitoring of environmental conditions (temperature, humidity) to reduce spoilage
- Recommendations for reducing plastic packaging waste
- Inventory insights to minimize overstock and reduce wastage
Imagine a 24/7 assistant for your retail operations — one that sees it all, processes data at lightning speed, and delivers deep, actionable insights.
It’s not magic — it’s computer vision, a game-changing tool for efficiency and problem-solving. Discover the ‘body’ and ‘soul’ of this technology and how it works wonders for your business.
Process description
Hardware and software needed
Example (inventory management use case)
Image acquisition
Process description
Capturing images and video footage of:
- Store shelves
- Products
- Customers
- Sales floor
- Backroom
- Interactions with products
- Checkout activities
Hardware and software needed
- High-resolution cameras
- Infrared or motion sensors
- Edge devices
- Network architecture
- Cloud storage
Example (inventory management use case)
Photos or videos of product shelves are taken.
Image preprocessing
Process description
- Noise removal
- Brightness and contrast adjustments
- Image sharpening
- Resizing
- Compression
- Edge detection
Hardware and software needed
- Gaussian blur
- Canny edge detector
- Histogram equalization
- Morphological transformations
Example (inventory management use case)
Algorithms enhance the quality of the captured images:
- Remove any graininess or blur (e.g., caused by low light)
- Adjust contrast to ensure product labels and barcodes are clearly visible
- Detects edges of products, shelves, and barcodes to help identify items on the shelves more effectively
Object detection, classification, and recognition
Process description
Identifying and localizing objects in images or videos.
Hardware and software needed
- CNNs and YOLO
- Deep neural network
- Visual transformers
Example (inventory management use case)
The insights you can get at this stage:
- Barcode and price tag reading, distinguishing product A from product B, identifying products on the shelf.
Advanced analytics
Process description
Analyze complex image patterns and video sequences to deliver insights or trigger actions.
Hardware and software needed
- CNNs
- Recurrent neural networks (RNNs)
- Generative adversarial networks (GANs)
- Machine learning libraries
- Integrations with internal systems
Example (inventory management use case)
The system can compare the photos of the shelves with the planograms and identify misplaced and missing items
3D vision and spatial analysis
Process description
Reconstructing environments and analyzing depth to understand spatial relationships.
Hardware and software needed
- Stereo vision
- LiDAR
- Time-of-flight sensors
- 3D modeling
Example (inventory management use case)
The computer vision system calculates the number of available items at the back of the shelf, understands if it’s a low-stock situation, and triggers a replenishment alert for the associate or a purchase order.
Need a clear view of your business case?
Our computer vision experts are here to bring clarity to every detail.
Steps to implement computer vision for retail
Define objectives
- Audit the current processes and operations to pinpoint the challenges you want to solve and the improvements you want to achieve with computer vision. For example, your customers may be dissatisfied with long wait times, so you want to introduce a faster checkout process with a self-checkout option.
- Outline all the use cases you want to implement. Think of tasks like inventory management, virtual try-ons, and theft prevention.
- Define success metrics for the project.
Check feasibility
- Check if you have enough representative data to train the computer vision model or if public data sets are available.
- Check data quality and plan a labeling strategy to ensure accurate and reliable annotations. · Ensure infrastructure readiness (e.g., network bandwidth, camera resolution).
- Test whether a pre-trained model can achieve the minimal quality threshold and evaluate whether a production-ready solution can achieve the required quality after the model’s fine-tuning.
- Calculate the investments needed to develop the solution and compare it with the expected ROI.
Decide on custom or OOTB
- Decide between out-of-the-box (OOTB) and custom computer vision solutions for retail. If you go custom, you’ll need to design the solution architecture, choose the tech stack, and train or fine-tune machine learning and deep learning models.
- If you prefer OOTB, find a suitable option and fine-tune models (if needed).
Integrate with your retail infrastructure
- Select the right hardware: high-resolution cameras and IoT sensors.
- Connect computer vision tools with your existing infrastructure and your retail-specific software. Depending on the use cases you pursue, the following integrations may be crucial:
- Point-of-sale (POS) software
- Inventory management
- Customer relationship management
- Video surveillance
- Supply chain management
- Data analytics
Pilot the system
Start small to ensure the system delivers exactly what you need. Test it in one store or for specific product categories to check:
- If the system is accurate in detection, classification, and recognition tasks, inventory counts, calculations, and recommendations
- If the system boosts customer experience and operations (based on analytics and feedback from store managers, employees, and customers)
- If the system needs optimizations of processing speed and scalability
Roll-out
Once the pilot is successful, scale the system across stores and monitor its performance to introduce necessary tweaks. Computer vision is not a 'set-it-and-forget-it' technology. Regular updates to AI models, incorporating new data, and refining algorithms ensure the system remains effective and adapts to evolving retail needs.
Hear from our expert
“When it comes to the usual dilemma — custom vs. out-of-the-box — it seems that life will be so much simpler with the latter. Indeed, out-of-the-box solutions can be a great way to start quickly, offering faster launch times and lower initial costs. While they’re a solid choice, it’s essential to think long-term and ensure the chosen OOTB product:
- Supports all the use cases you’re planning to implement
- Smoothly integrates with your existing retail software, especially if you’re using custom or legacy software
- Doesn’t require costly and time-consuming customizations to meet your goals — and confirm that customization is even an option
- Delivers the expected accuracy when working with your data.
If you believe your OOTB solution meets these requirements, it’s likely to fit your business today and as you grow. If not, there’s hardly a better option than a custom solution.
Custom solutions open unparalleled opportunities: they enable tailored functionality, integrate seamlessly with your ecosystem, and scale effortlessly to match your growing needs. Sure, they require a higher upfront investment, but their ability to tackle unique challenges makes them worth every penny.”
Alexandr Yakovlev
Director of Engineering
Integrations to consider
To deliver the functionality you need, your computer vision software should seamlessly exchange data with your internal systems and even leverage other advanced technologies.
Point of sale system
Ensure all the items are scanned at checkout and help identify customers through face recognition.
CRM
Enrich customer profiles with behavioral insights to provide personalized service to loyal customers or make targeted offers.
Inventory management
Understand stock levels, product turnover, planograms, and replenishment schedules to monitor and manage inventory counts in real time.
Augmented reality
Match product, customer, and environment visual data with AR visuals and have virtual try-ons, product demonstrations, or in-store navigation.
Wide adoption and proven benefits of computer vision in retail
The secret behind Amazon, Sam’s Club, and IKEA’s retail dominance? Computer vision. Here’s why it works.
Adoption boom
According to Gartner’s Hype Cycle, computer vision technology will reach its plateau by 2025, signaling its inevitability as a standard technology in retail.
Market set to soar
The global computer vision market is projected to grow from $25.8 billion in 2024 to $46.96 billion by 2030, with a compound annual growth rate of 10.5 percent.
Retail’s next tech standard
In 2023, nearly 40 percent of retail directors stated they used artificial intelligence, computer vision, and machine vision for selected operations, with 35 percent scaling up these technologies and 15 percent planning implementation within the following year. Computer vision is quickly becoming the new norm in retail.
Lost sales prevention
A study by the IHL Group revealed that retailers miss out on nearly $1 trillion in sales per year because of out-of-stock items ($144.9 billion in North America alone). As computer vision technology serves to fight out-of-stock, it has the potential to prevent lost sales.
Seamless self-checkout
While 73 percent of consumers prefer self-checkout over traditional staffed registers, theft increases by up to 65 percent at self-checkout compared to a traditional checker.
Computer vision technology allows controlling the self-checkout process, enabling customers to enjoy the experience they prefer.
Inventory management automation
At Sam’s Club, robots automate 35 percent of inventory management tasks. The retailer also reports a 23 percent faster exit process thanks to computer vision.
Hear from our expert
Key to successful computer vision adoption is high output accuracy
In retail, the success of computer vision adoption hinges on the system's ability to recognize products, identify brands, and count inventory correctly.
Is this achievable? Yes, but the degree of the invested effort may vary.
Pretrained models specifically designed for retail computer vision tasks are available, and the good news is they often deliver the needed accuracy right from the start. Still, they may fall short if your products or tasks are unique. In such cases, pre-trained models can still be a valuable starting point, as fine-tuning them is significantly faster and easier than training a model from scratch.
However, even the best model won’t perform well without high-quality data. If your data set is scarce or full of mistakes and missing values, the case may require extra work — think of data set augmentation by artificial techniques and massive data annotation efforts.”
Darya Krauchenia
Senior AI Expert
When to implement computer vision: a checklist
Drawing from decades of experience, we’ve pinpointed key scenarios where computer vision technology can deliver exceptional value. If your business fits any of these, it might be the right time to explore implementing computer vision:
You offer a wide product range and experience high turnover.
One or several retail processes are too resource-consuming, which negatively affects either your employees or customers (or both).
You cannot efficiently cope with theft or shrinkage using traditional methods.
You face frequent out-of-stock situations causing lost sales and customer dissatisfaction.
You are experiencing or planning a solid expansion of your retail stores.
You lack the sync between brick-and-mortar and online channels
Your customers value innovation.
Cost factors and hidden costs
"I need computer vision software for my retail stores”, you’ll say. What’s next? What budget should I plan?
Here are key budget items to consider:
- Hardware: cameras and IoT devices.
- Licenses for technologies required to build your computer vision solution, unless they’re open-source and therefore free.
- Costs of a ready data set or data preparation that can cover data collection, annotation, and anonymization efforts.
- Operational expenses: payments for the storage and computing resources (at least) to process all the videos and images taken.
- AI model creation: even if you go with a pre-trained model, there’ll be costs of fine-tuning it to meet your needs. And if you need to develop a model from scratch, this will add to the costs even more.
- Custom integrations or third-party APIs.
- Infrastructure setup, covering IT network readiness and camera installations.
However, to make an informed decision, you’ll also need to consider hidden costs:
Software maintenance
It’s a common misconception that once software is developed, you no longer have to invest. In reality, software maintenance can account for over 90 percent of the total cost of ownership — the numbers that can be neither ignored nor underestimated.
And one thing is certain: you’ll inevitably need maintenance, as you want your solution to function perfectly even when time passes and align with your evolving business needs:
- Introducing a new product or reconsidering the store layout? Your computer vision model should be retrained.
- Want to get insights faster? The data pipelines should be tweaked.
- Adding new store systems? You might also need to set up new integrations.
Hardware maintenance
Cameras and edge devices need periodic cleaning, calibration, repair, or replacement.
In-store preparation
While installing cameras is an obvious step, you’ll need to do much more. For example, ensure that your facilities have sufficient lighting, as it directly influences the quality of the images/video and the solution’s accuracy.
Looking for a reliable computer vision company? Consider Vention’s services
Consulting
Overwhelmed by endless strategic decisions?
We’re here to simplify the process — choose the right tech stack, analyze feasibility, or craft a solid project plan. Our team ensures your project starts on solid ground and ends in success.
Custom development
We design and develop custom computer vision solutions tailored to your retail needs.
Inventory management, cashier-less checkouts, customer behavior analytics — you name it, and we deliver software that achieves your goals.
Integration
Need a solution that smoothly fits into your environment?
We integrate computer vision software with your existing systems, whether they’re POS, inventory management, analytics platforms, or CRMs—even custom or legacy ones.
Support and evolution
Worried your solution won’t keep up with the times?
Not with us. We provide ongoing support to fine-tune algorithms, optimize performance, and expand features as your business evolves — ensuring your solution stays ahead of the curve.
Why us? Because we deliver truly visionary results
Years of experience in custom software development
Engineers with AI-specific skill sets
AI projects successfully finished
Robust security practices recognized by an ISO 27001 certification
