Computer Vision: Coming to a Store Near You

Computer Vision: Coming to a Store Near You

Thursday, March 21
2:50 PM - 3:30 PM
Room 124-125

Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers

Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.

Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.

We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.

We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.

Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.

By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in Consumer Goods, key use cases, techniques and key considerations leaders are exploring and implementing today.

Presentation Video


Brent Biddulph
GM, Retail & CG Solutions
As a former retail and consumer goods executive and more recently as a business strategy consultant and solution provider, Brent has extensive experience working with a variety of retail and consumer goods companies to provide thought leadership and help them to align strategic business objectives with technology and analytic solutions to create a differentiated competitive advantage in the marketplace. He has an extensive track record of imagining, designing and executing high impact business solutions, driving innovation and transformation for retail and consumer goods organizations. Brent is passionate about analytics, emerging technologies, consumer behavior, collaborative supply chains and retail transformation. As General Manager of Retail and Consumer Goods Solutions at Hortonworks, Brent is responsible for driving the solution vision and go-to-market strategies with each segment. As industry leaders increasingly invest in Big Data Analytics to help drive transformation within their organizations, Brent engages globally to share, discuss, provide keynote talks, and facilitated workshops to help define and create solutions to drive next-generation insights and positive business outcomes across the value chain.
Florian Muellerklein
Data Scientist
Miner & Kasch
I am a data scientist with Miner & Kasch, a data science consulting firm. I specialize in developing automated solutions for our clients using machine learning, specifically in the domains of computer vision and natural language processing. Additionally, I lead the deep learning training sessions that Miner and Kasch holds. Across a variety of domains I have successfully applied deep learning to computer vision problems involving image classification, object detection and segmentation. For Natural Language Processing tasks I have created neural information retrieval systems, semantic similarity search engines, and question answering systems. My favorite machine learning techniques are representation learning methods that result in surprising and useful latent variables that facilitate higher level tasks.