How retail can leverage Computer vision?

Photo by Viktor Bystrov on Unsplash


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Challenges in Retail that CV can solve
Real time Inspection and Management
  • Lack of real-time store insights
  • Limited product analytics
  • Limited customer analytics
  • Security challenges
Final words

Computer vision allows computers to learn from visual data such as video feeds or images and provides us with deeper insights and many practical use cases that can be deployed on the field. It leverages Artificial Intelligence and deep learning techniques to understand the visual insights from images to help us build business-specific solutions.

CV solutions can be deployed in multiple industries, from manufacturing and energy to healthcare and retail. Among them, the retail industry has found CV to be applicable in various profit-generating ways to increase revenue and productivity.

It is estimated that the computer vision and hardware market will reach a staggering USD 48.6 billion by the end of 2022 and is predicted to grow at a rapid pace in the future as well. And mind that these are the numbers just for the computer vision segment and not including the machine learning use cases of which there are many – such as demand forecasting, inventory management, etc.

All this suggests that there is an urgent need for retailers and store owners to adopt AI, or to be specific, computer vision, to stay ahead of the curve and gain a competitive edge in the market. We will look at how can the retail industry do so throughout this blog.

Challenges in retail that CV can solve

Any construction project requires extensive site identification and analysis. This includes understanding land contours, vegetation, and so on. Traditionally this is performed manually, with little technology involved. However, introducing a Computer Vision equipped drone can turn out to have multiple benefits compared to manual surveying. 

A drone can ariel views of the concerned land from micro and macro perspectives. This visual and spatial input from the multiple sensors and cameras of the drone can be analyzed by computer vision to determine multiple parameters required in completing a survey. 

From an architectural standpoint, Computer Vision equipped drones can be used to identify land textures, contours, and elevation of a concerning piece of land. Computer vision can help analyze the input to understand the multiple parameters of the land and compare it with desired parameters.

From a commercial standpoint, Drones can be used to assess the architectural context of the land. This involves understanding the nature of surrounding structures, assessing accessibility to the proposed building, and understanding any potentially unfavorable structures to the proposed project.

Computer vision can further help compare these real-time parameters with favorable parameters to determine the feasibility of undertaking a project on this land. Such AI-assisted surveying saves time, and human and economic resources required for the project while minimizing any potential for error. 

Real-time Inspection and Management

Lack of real-time store insights

Using the traditional setup of cameras all over the place, the only information that a store owner or a manager will get is whatever the camera captures. There is no deeper level of insights outputted by this system and in most cases, the cameras are used just to keep a watch in different sections for security reasons.

Deploying CV will help go deeper within the visuals captured by the cameras and will allow us to use these insights for better control and tracking over the entire store. For instance, with CV, stores can keep a track of the number of people in the store, understand the foot traffic through different aisles, identify the staff and do much more.

Limited product analytics

With the current system, there is no proper insight into the product of the stores. Stores can’t keep track of the most in-demand item and also know what makes the customer buy a product.

With CV, stores can analyze shelves to determine the most in-demand product, determine product availability to get proper timings for restocking empty shelves, identify price tags, etc.

Limited customer analytics

With customer analytics using CV, stores can identify customers through their faces (obviously with prior consent) and record various features of a customer such as their age or gender. Based on this they can understand what products appeal to a specific demographic and can know how to market their products better for their suited customers.

With emotion recognition using CV, stores can look at customer sentiment while purchasing a product, looking at an advertisement, or interacting with the staff, in real-time. This helps stores to better analyze and improve on the things and services that lead to negative emotions such as disgust, awkwardness, anger, etc.

Security challenges

Within a retail store, even though there are multiple security systems already set up, it has been found that these security measures are not enough to maintain and secure the store properly. With CV, stores can ramp up their security measures to automatically detect theft, suspicious behavior, detect weapons, etc. to be aware of such situations in real time and inform the authority instantly.

Final words

Throughout this blog, we saw how retailers can leverage computer vision solutions to their advantage in various ways. We looked at the challenges that are currently possessed by the retail stores and saw multiple solutions derived from the CV that help solve them efficiently.

Still, deploying such solutions requires proper planning and effort to build a solution that works the best. There are multiple different challenges with that too and many companies are heading towards using a simplified computer vision platform for this. Read more about it over here.

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