Computer Vision in Retail: Top 5 Applications, Use cases and Future Trends

Introduction

Computer vision is a branch of artificial intelligence that gives computer systems the ability to extract, interpret, and understand useful data from visual input. The groundbreaking technology mimics human visual perception to capture, process, and examine visual data from images or videos. It then extracts useful insights from these datasets including recognizing objects, tracking body movements, gestures, and facial expressions. Retail owners can use this extracted data to improve and automate complex retail operations and enhance the shopping experience of customers. Therefore, understanding the nuances of this cutting-edge technology is imperative to stay ahead in packed retail space.

How Does Computer Vision Enhance Retail Experiences?

Integration of AI technologies in retail spaces, specifically computer vision technology is reshaping the retail environment. It applies deep learning models to train systems on how to spot objects and people, track their movements, and evaluate behavior patterns through visual input. In stores, this tech helps automate operations and boost customer shopping experiences. Retailers can also apply it to optimize the store layout and staff positioning to increase operational efficiency. From inventory and stock management to in-store advertising, and self-checkout kiosks to AR-powered virtual try-on solutions, computer vision is redefining every aspect of retail marketing and shopping. Let’s have a detailed look at useful applications of computer vision in the retail industry.

5 Popular Applications of Computer Vision In Retail

1. Self-Checkout and Cashier-Less Kiosks

Computer vision technology has completely changed the traditional checkout process by making it fully automated. Self-checkout systems use computer vision and deep learning models to automatically detect the products and their prices. When a shopper picks his desired products in a retail store, its camera systems capture images of each product. Then, vision algorithms are trained to identify the selected products and their prices to calculate the total bill. Computer vision systems very precisely associate checkout transactions with particular shoppers to avoid any misidentification and confusion. Moreover, analyzing customer behavior and patterns helps prevent theft or other suspicious activities. For example, Amazon’s Just Walk Out systems combine deep learning models with sensors and cameras to create a smooth self-checkout experience. Customers can pick up their desired products and go home without standing in long lines to pay their bills. Instead, computer vision uses precise object-tracking technology to identify a person’s movements and selected products. Then, the customer’s card is automatically charged for the total bill of picked items.

2. Autonomous Inventory Management

Modern retailers are integrating vision algorithms into the inventory and stock management system of their stores. This helps them keep track of inventory levels by monitoring store shelves. For this purpose, AI-powered smart cameras use computer vision to continuously scan the store shelves. This lets them detect any out-of-stock or misplaced products. If there are any gaps or inconsistencies on shelves, the AI system gives instant alerts so products can be restocked on time. Some major retail stores also employ autonomous inventory robots equipped with 3D cameras and computer vision technology. These robots roam through the store aisles, monitor low inventory levels, and issue replenishment alerts. This not only prevents revenue loss but also enhances customer satisfaction by ensuring all-time product availability.

3. AR-Powered Virtual Try-On Experiences

Combining computer vision with augmented reality creates an immersive online shopping experience called a virtual try-on. AR superimposes digital elements on real-world surroundings and blurs the line between imagination and reality. This lets users virtually see how different products (e.g., clothes, jewelry, and make-up items) look on them in lifelike settings. The virtual try-on experience is made possible through AR-powered smartphone apps, smart mirrors, or virtual fitting rooms. This trend is widely popular in the fashion sector, where customers prefer virtual try-ons before making any purchase. For example, Ralph Lauren has introduced a virtual fitting room powered by computer vision algorithms. When a customer tries any outfit in that virtual fitting room, machine learning algorithms provide real-time data about its available sizes and colors and offer personalized product suggestions that go well with that outfit.

4. Personalized In-Store Advertisement

Using a variety of sensors and smart cameras, computer vision algorithms collect and analyze precise details about customer behavior and shopping patterns. It monitors which products customers view, pick, eventually buy or put back. By analyzing a person’s complete shopping pattern and history, vision algorithms help AI systems create a custom user profile. This personalized profile can be helpful in providing tailored marketing experiences to these customers in the future. Moreover, this innovative technology also enhances geofencing. This enables retailers to identify certain customers as soon as they enter the store and offer them special discounts depending on their previous shopping history and personal profile.

5. Retail Heat Maps

Retail heat maps provide valuable insights into customer behavior and movement patterns within retail stores. Using computer vision cameras, this technology generates a visual display of customer traffic by tracking their real-time movements and allocating shades to different floor areas depending on the traffic volume there. Computer vision algorithms help retailers identify "hot spots" where customers tend to engage more, track their purchase patterns, and analyze product interest. Using these details, retailers can also optimize staff positioning and store layout for effective merchandising. Many major retail brands, such as Sephora, use retail heat maps to track their store functionality and optimize their operations.

Future Prospects of Computer Vision In Retail: What Lies Ahead?

Computer vision is, without a doubt, one of the most advanced AI technologies of this era. It has many practical applications in many sectors such as retail, health, education, and entertainment, etc. However, there are certain situations that cannot be detected or tracked using vision algorithms. For example, computer vision cannot accurately examine areas with poor lighting, high traffic, or complex environments. This can lead to incorrect detection and identification of products or individuals. However, the self-iterative nature of this cutting-edge technology means it is constantly evolving and getting better each day. Moreover, its integration with advanced Metaverse technologies like AR, VR, and MR will open up unimaginable retail solutions for customers and retailers like never before.

Conclusion

Employing computer vision in retail goes beyond capturing images and videos. It transforms customers’ shopping behavior and patterns into actionable insights, which can be used to streamline in-store operations and automate complex tasks. Moreover, this helps retailers enhance customer’s shopping experience by making it more personalized and easy. Therefore, computer vision is not just a hot trend, it has become a must-have for retailers to expand and grow their businesses. If you are thinking of employing advanced vision algorithms in your retail business, feel free to contact us. Our vision experts would love to help you achieve your e-commerce goals quickly.