The Power of Video Annotation in Retail: How Labeling Videos Can Enhance AI Systems and Boost Business Performance

Currently, the world is facing a shopping boom, with new products hitting the market every day. Supermarkets are beginning to use computer vision as a method of reducing costs and promoting a better customer experience. AI-powered cameras can help to prevent theft, identify in-store traffic patterns, and even discern how customers are feeling.

Although busy retail settings are often full of complex movements and interactions because of their dynamic and exciting environments. 

For AI models to function properly, they need to be trained using video data that has been annotated by humans. Annotators add information to every frame of footage, which allows AI models to identify objects and movements in the real world.  

Using video annotation improves the quality of AI systems in many ways that benefit both companies and consumers. Therefore, in this blog, we’ll discuss video annotation and how it’s critical to improving retail AI systems.

Video annotation and Retail AI

Video annotation is the process of adding labels or descriptions to video content to improve its search ability and usability. Retail AI is a branch of artificial intelligence that applies AI techniques to the retail industry.

The two technologies are often used to create intelligent use cases from videos, such as object identification and movement tracking in supermarkets. 

For example, video annotation can add product labels in a retail setting. Retail AI algorithms can then be used to understand and predict consumer behavior. This combination of technologies can create a more seamless and personalized consumer shopping experience.

How video annotation helps improve retail AI 

1. Sentiment analysis

Sentiment analysis uses automated tools to identify the sentiment expressed in a text, such as an image or video. Sentiment analysis can determine whether customers are happy with a product or service or have problems with it.

Stores using video annotation for sentiment analysis of customers.

For example, a retail company might use video annotation to analyze customer behavior in their stores. They could annotate specific frames of the video to indicate when a customer is smiling, frowning, or showing other emotional cues. This information could then be used to evaluate the customer’s overall sentiment and determine how effective their in-store experience was.

2. Monitoring in-store traffic

Video annotation is a powerful tool for monitoring in-store traffic. This method lets you see exactly when people are walking by your store and which areas attract their attention.

Using video annotation in stores to monitor large crowds.

For example, if you want to know how many people are stopping at your display window, you can use video annotation software to count them as they pass by. You’ll also see how long they stay there and what they do while looking at the product.

Loss Prevention

Video annotation is a great way to combine AI and machine learning with human intelligence. The technology allows you to enhance your video datasets with valuable information that can help you better understand what’s happening in your store.

Using video annotation to prevent loss or theft of products

Retail AI’s understanding of thefts is especially important for loss prevention, which can be a major challenge for retailers. According to statisticbrain’s data, employee theft costs U.S. retailers $50 billion a year, showing the growing need to counter theft. 

The software can also identify individuals with a history of theft or shoplifting and alert store employees when those individuals enter the premises.

4. Skeletal

Skeletal annotation is a technique used to help computer models understand the movement of human bodies. This is done by adding lines to human figures in video frames, which creates a simplified shape that artificial intelligence models can interpret.

5. Keypoints

Point annotation is the process of identifying certain features by pinpointing them with dots. This is most commonly used for facial recognition AI models. By constructing an image of a human face with points or dots, AI models can be trained to recognize it more easily.

How to choose the right video annotation providers

There are a few things to consider when choosing a video annotation provider. 

  • First, what type of annotation do you need? Human annotators create the video data and they specialize in different annotation types, so choosing one that can provide the specific type of training data you need is essential. 
  • Second, what is your budget? Video annotation can be costly, so it’s important to find a provider that fits your budget. 
  • Finally, what is your timeline? Some providers may be able to turn around your project faster than others. So, if you have a tight timeline, choose a provider that can accommodate your timeline.

When it comes to best-in-business, Shaip is a video annotation company that provides access to advanced video annotation solutions to help you create perceptive and intelligent models. Shaip’s model training power is fortified with data mining tools, in-house data labeling teams, and a wide range of video annotation tools that can be customized to suit every relevant use case.


Video annotation technology is meant to keep retail AI systems and customers safe. Video annotation software is a great way to do this by 

  • Letting people quickly and easily alert authorities when they witness something suspicious in a retail setting and;
  • Helping AI systems learn from past experiences so they can tailor their responses to feel better about what is considered normal behavior. 

It’s a win-win for everyone involved, although video annotation for retail remains somewhat underrated.

Author Bio

Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.

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