Computer Vision Sees Better Than 20/20

Ray Hartjen
Ray Hartjen
Guest Contributor

In-store analytics integrate a variety of sensor technologies, and more come online seemingly every day. But, video remains one of the more powerful data streams, and computer vision converts the feed in valuable, useful data for managing stores.

RetailNext integrates a variety of sensor technologies as part of its “technology stack” in building its industry-standard retail analytics platform. Data sources and streams are abundant, and as the Internet of Things (IoT) continues to explode in growth, useful new data continually comes online.

Paramount, of course, is the concept of “useful,” and with retail analytics it’s all about gathering, analyzing and reporting data that helps retailers better manage their stores.

Some of the most useful data comes from video analytics, and as a data source, video is extremely powerful. But, how does the video feed turn into useful data?

Enter “computer vision.”

In the video below, George Shaw (@kngpengwin), RetailNext’s head of Research & Development, provides a quick overview of computer vision, and how it goes pixel-by-pixel, frame-by-frame to algorithmically generate data that can make all the difference in more effectively creating and managing a productive in-store shopping experience.

Video analytics, derived through computer vision, helps retailers answer many critical questions, including:

  • How many shoppers entered the store?
  • What are my shoppers’ gender and age ranges?
  • Where do shoppers go in my store (and where do they not go)?
  • Where do shoppers stop and engage with fixtures or sales associates?
  • How long do they stay engaged?
  • Which are my most effective fixtures, and which ones are underperforming?
  • And many, many more

Computer vision helps provide the answers to retailers’ most pressing questions, and it does it automatically, at scale, using algorithms to generate data all from a digital video signal. If video analytics is new to your retail business, contact us for how to quickly get started.

Join the #retail conversation with @RetailNext and @kngpengwin on Twitter, as well as at