Computer Vision in Retail

The modern wave of interest in artificial intelligence has expanded its possible applications while also making it a bit more accessible to smaller businesses. One result of this is the spread of computer vision technologies in the retail space. What may sound like futuristic technology is already being rolled out across different chains, and today, we’ll show how you can apply it.

This article explains computer vision in retail, its advantages, possible use cases, and challenges. We’ll break down the reasons to adopt this type of AI in your operations and offer a balanced view of its potential for retail businesses. Once you know all the possible pros and cons, we’ll gladly help you implement the tech.

For now, though, let’s get started with a simple definition.

What is Computer Vision in Retail?

Computer vision is an intersection of AI technologies that help computers “see” the world through intelligent object scanning and processing. Using machine learning, models fill in missing information, allowing them to view specific objects as full 3D copies. Through this, computers understand their dimensions and qualities, recognise patterns and materials, calculate quantities, etc.

While computer vision is useful across the board, from medical professionals scanning internal organs to automated cars analysing the road, retail is our topic of choice today. In this field, vision is used to validate products, analyse and influence customer behaviour, and track inventory.

We’ll highlight this and other use cases further in the article. For now, though, let’s talk about how computer vision works and what its architecture is like.

Core Technologies Used in Computer Vision

This applies to computer vision in retail and other industries, as these things are the base building blocks and largely remain unchanged. First up, several libraries are typically used:

• TensorFlow — an open-source framework that supports deep learning;
• PyTorch — another open-source library for AI, one of the most popular ones out there;
• OpenCV — specifically designed for computer vision, also open-source;
• Scikit-image — an image-processing open library, used by Google, among others.

These four help train your network and recognise different aspects of images and videos it processes, including colors, spatial dimensions, morphology, and others. If your desired use case includes character recognition and processing, you will also need Tesseract OCR. This free tool is indispensable for document scanning and “reading” product labels with computer vision.

Then, there are several deep learning models you will rely on:

• Convolutional Neural Networks (CNNs);
• Generative Adversarial Networks (GANs);
• Variational Autoencoders (VAEs);
• Vision Transformers (ViT);
• Vision Language (VLs).

Your specific case may not involve all of these. But, just so you know which you may want, here’s a brief breakdown. CNNs are key if you want to teach your model to organise spaces and analyse them. GANs are for image creation and structuring. VAEs enable smarter encoding that cleans up images and video, including removing noise and artifacts. ViT helps transform images to 3D models and create sequences, while VLs add language processing.

Data Acquisition Methods

The first method, and the most straightforward one, is to simply get a direct feed of visual data from your in-store cameras and sensors. It’s easy to set up real-time acquisition, and this will be immediately relevant to your operations.

Next, you may also have a setup where data is streamed over the internet, compiling sources from many different locations. This is a slightly more complex architecture, but it’s pretty much non-negotiable for retail chains.

You can also use data already stored in your databases across your network. This is important for training the model and analysing changes over time. You don’t have to rely solely on data past the point of CV introduction, as information from previous years may prove vital.

Benefits of Computer Vision in Retail

Now that we’ve dived into the structure of computer vision and how it functions, let’s get to the most pleasant part—listing the advantages that computer vision brings to businesses. We’ll focus on the most commonplace and meaningful ones, so this is not an exhaustive list.

Higher Operational Efficiency

Computer vision simplifies and streamlines a wide range of processes, whether through automation or by providing more in-depth analytics. That helps avoid situations where minor operational issues go overlooked and snowball into major issues. When set up properly, computer vision functions as a potent early warning system.

Having the ability to delegate minor tasks to an automated algorithm is already a time-saver, but it’s further enhanced when team members focus on other, more complex work. As a result, the business can operate more quickly and avoid situations where tasks pile up. The most time-consuming and tedious tasks, such as inventory management, will be handled entirely by your AI systems.

Lower Expenses

As a direct consequence of automating processes and making more data-based decisions, retail businesses can end up substantially cutting their operational costs. For example, day-to-day operational workload can be reduced as the system automates repetitive in-store activities. Then, analytics can help eliminate unnecessary maintenance costs. Sometimes this is done through more effective predictive maintenance, in other cases, via smarter need-based device usage.

Increased Revenue

On the flip side, the financial upsides of computer vision and its analytical power can boost sales and overall income. Achieving this will take some time, but it’s a lasting effect, as you apply insights from computer vision to your operations. One example would be tracking customer behaviour and redesigning the store layouts to encourage more buying.

Another way is to minimise stock spoilage through smarter inventory management, which computer vision is practically tailor-made for. This will ensure that your retail business is selling any inventory that’s in danger of being written off, as well as keeping shelves stocked in high seasons.

Quicker Decisions

AI, in general, is great at processing data and distilling massive datasets into quick insights you can implement in your business. Computer vision is no exception, as it can help instantly analyse inventory, store layout, marketing efficiency, and staff performance. This will be based on how quickly certain products are sold, how customers move through the space, what draws their attention, and other factors.

Smarter Branding

Another benefit of computer vision in retail market is the ability to assess exactly how customers interact with your brand and products. From tracking how long a person looks at in-store and external advertisements to analysing the spaces they tend to observe. The latter is helpful as it allows you to place high-margin products there or branding elements that highlight the store.

What’s important to note, before we proceed, is that all of these benefits can be mixed and matched depending on your needs. A computer vision system can be tuned to help you increase sales only. Yet, it can just as easily serve as a decision-making and cost-cutting tool. The choice is yours, and it’s all about how you integrate this tech into your ecosystem.

Use Cases

The only way to get all the benefits we’ve just discussed is to get computer vision working for you. That can be done in a variety of ways, each with its own complexities and strengths. The following section will introduce you to some of them and outline their effect on your business.

Visitor Profile Analysis

Potential applications of computer vision in retail store chains start at the very entrance to the location. As customers enter the area covered by your CV-enabled cameras, the system can scan them and analyse visual data to create a series of typical visitor profiles. A selection of specific visitor types will be broken down by factors like:

• Appearance;
• Time of visit;
• Initial behaviour.

These are then used to not only target your marketing better but do initial assessments of how likely someone is to make a purchase, what their areas of interest may be, etc. By splitting the typical daily attendance into these profiles, you can also narrow down your “ideal visitor” type. These would be the people most likely to enter the store and buy something instead of just browsing.

Customer Behaviour Analysis

Similar to the profiles that help you understand your clientele, behaviour analysis will highlight your stores’ strengths and weaknesses by dissecting your average customer. You will use visual data to analyse how a customer interacts with the space, their body language, and the factors that influence shifts in their behaviour.

The model can correlate negative behaviour markers with customers seeing low-quality products or poor floor planning, as well as a litany of other minor details. This helps eliminate any guesswork in optimising the store layout and the products you choose to stock. Plus, it will indicate how successful your business is—the more positive customer behavior you’re seeing, the better.

Customer Experience

Understanding customer quirks and the causes of their behavior will also allow your computer vision system to analyze their needs and how to meet them. This can take many forms, depending on your customers’ ages, income levels, locations, cultural expectations, etc. Some examples would be:

• Minimising/maximising automation;
• Stocking more high-end goods;
• Providing more staff helpers on the store floor;
• Extending opening hours;
• Adjusting in-store lighting, music, etc. (ambiance).

All of these small tweaks can be meaningful ways to enhance the average customer experience, and analytics will guarantee you’re making the right choices. The automation point is an excellent showcase for this, as most companies would assume automation is good for business. However, stores with a technologically averse target audience, usually older generations, would benefit from a focus on warm, inviting human staff.

Customer Service Quality

Speaking of staff, to study what they’re doing right or wrong, you can use computer vision to track and analyse their behavior, just as you would customers’. In this case, you’d be looking at the way interactions between employees and customers go, assessing what creates positive effects and what may sour the experience.

Using data gathered from this, as well as typified customer profiles, you can introduce employee training aimed specifically at boosting service quality. In some cases, this will simply be about being proactive; in others, a change in how fast interactions go. After all, some retail customers may prefer to slow down and make their shopping experience more social.

Product Engagement Analysis

There are plenty of possible reasons why specific products underperform, some of which depend on factors outside your control, but others are completely fixable. Computer vision helps in this area, too, by offering you a zoomed-in view of how customers interact with products. Things like checking the price tag, reading the ingredients label, and putting the item down. Meanwhile, algorithmic functions will use the gathered info to assess whether the goods could be marketed better.

Inventory Management

Computer vision systems are capable of scanning a space, in our case, a warehouse, and estimating how many items are “missing”, thus instantly calculating available inventory. This also indicates how much stock needs to be ordered. That data is then sent on to the automated system that places requests with your suppliers and processes their invoices.

The beauty of this is that it can all be completely autonomous, requiring no human intervention to keep the store stocked, as always. In fact, the system can issue suggestions such as booking extra stock for seasons with high foot traffic (holidays, sales periods).

Products Quality Control

While most quality control should occur at manufacturing sites using computer vision and human monitoring, it never hurts to double-check. This is especially crucial for groceries and products with strict expiration dates. For expiration dates, the process is as easy as the system viewing and recognizing the relevant characters with OCR, comparing them against its databases.

You can also achieve more refined quality control, such as detecting physical damage and other issues like discoloration, distension, or incorrect labeling. This also involves comparing the analysed product with the correct model, with computer vision treating both as 3D objects.

Shelves Management and Planogram

Marketing your products is also easier with computer vision in retail, as the systems can analyze where visitors look most often and how much foot traffic each aisle gets. Based on that, you can place products around the store to maximize potential interest and sales. The system can draw up multiple planograms that organize your stock according to priorities:

• Selling off excess stock;
• Matching brand deal terms;
• Maximizing revenue.

Virtual Try-on Experiences Enabled by Augmented Reality

Retail chains focused on clothing, accessories, or home decor can benefit from an entirely different side of computer vision. Using AR technologies, you’ll be able to offer customers the ability to try on a particular item of clothing or, by scanning a room, see how a piece of furniture will look in it.

These methods are already used by major brands such as Zara and will only improve as AI models are refined. One important point is that this requires collecting identifiable data such as users’ body scans and photos, which must be processed securely and legally. If your brand wishes to implement this, it’s best to partner with an experienced vendor and consult a legal professional.

Checkout-Free Systems with Computer Vision

Customers can forget about lines at the checkout and having to wait for products with damaged barcodes to be logged into the system manually. Using CV, shops can now scan the premises, detecting what each visitor has put in their basket, and automatically calculate their bill. Once a person is done with their shopping, they simply pay via an app or a card tap at the entrance.

AI-powered Inspections Conducted Both In-store and On-site

Similar to quality control, computer vision can serve as a tool for remote and in-person store inspections. The system scans the premises, shelves, employees, and products to make sure everything is clean, well-organized, and not damaged. These inspections can even be fully automated, with human staff simply monitoring the results and confirming their authenticity.

This is especially useful for those who wish to conduct surprise inspections, ensuring their stores are always in proper condition and not just on preset inspection days. You’ll then be able to see a more stable level of organization and cleanliness on the premises.

Security and Loss Prevention

Smarter cameras mean better theft detection and early warnings for security staff. Computer vision can detect someone concealing an item in their clothing or trying to exit the store with a product that wasn’t registered in any of the paid orders. These systems can also rely on biometric data to instantly spot potential or confirmed troublemakers.

You can also apply computer vision for security in budget-friendly ways by strategically placing cameras in high-risk areas, such as automated checkouts and low-visibility parts of the store. This way, you need fewer devices by focusing your system’s “attention”.

Challenges of Computer Vision in Retail

It’s not always easy and smooth to integrate computer vision in retail and reap the benefits. Like any technology or transformative step, it comes with its own possible set of obstacles. We’ll cover a few of them in this section to prepare you for these problems and show you how to tackle them successfully.

Legacy Integrations

Many retail chains still use legacy point-of-sale systems and ERP software, as well as outdated inventory management solutions. Combining these with modern computer vision systems can be challenging, as you may encounter compatibility issues or need to upgrade your ecosystem.

In these cases, we suggest erring on the side of progress and going through with modernization. It may be complex, but the result is a more functional and reliable system.

Strict Monitoring

Your initial usage of computer vision systems should include constant monitoring of the system’s performance and output. As the models rely on plentiful data sets to improve, it may take a bit of time before they’re giving optimal advice and making the best decisions. Thus, employees should double-check their analytics and handle inventory management first.

Data Quality

Odds are your current setup doesn’t involve constant organization, labeling, and saving of visual data—images and videos, especially from in-store cameras. This will have to change as your AI model requires plenty of high-quality, carefully selected data with unified formatting. Setting up this pipeline for training may be time-consuming but, with the right partner, quite doable.

How to Get Started with Computer Vision in Retail?

This wraps up our guide to computer vision in retail and its different sides - the good and the bad, the potential use cases and possible challenges. It’s now clear that a properly implemented computer vision system can positively affect a retail business top to bottom.

From studying customer preferences to managing stock to enabling smooth, autonomous checkouts, there is an entire range of opportunities for transformative CV applications. Using our selection of use cases and the information on avoiding typical pitfalls, you can get started on this venture. Alternatively, you may want to ensure success by partnering with an expert team.

Integrio Systems is a company with over 25 years of experience in custom development, consulting, and outsourcing, delivering quality and dedication. Our team members always prioritise the client’s wishes and needs, while providing their own expert input. With consistent reporting and smooth delivery pipelines, the results speak for themselves.

Our AI projects emphasise an ethical approach, smooth CI/CD pipelines, and long-term cooperation to ensure you’re getting the most out of the technology. However big a scope you have in mind and whatever pace you want to take with this project, we’re ready to help. Integrio Systems doesn’t shy away from ambitious, challenging work and projects that nurture business growth.

So get started on your computer vision journey with a trusted partner by your side. Reach out to Integrio Systems to begin.

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