See Sumedh Datar and many other expert speakers at the 2023 Embedded Vision Summit!
Computer vision has vast potential in the retail space. At 7-Eleven, we are working on fast frictionless checkout applications to better serve customers. These solutions range from faster checkout systems to fully automated cashierless stores. A key goal for such solutions is to ensure high accuracy and consistent customer experience across thousands of stores. In this talk, we will focus on how we have built scalable item recognition models and algorithms that work on tens of thousands of products. We will discuss the challenges we face in building practical, edge-based solutions — such as the need to recognize thousands of items with varying packaging and sizes, and the need to deploy systems on constrained hardware — and we’ll explain the techniques we’ve employed to overcome these challenges.
Sumedh Datar is a computer vision researcher with six years of experience in applied deep learning and computer vision, delivering edge-based solutions for real-world problems. Prior to his current role at 7-Eleven, focused on retail applications, Sumedh worked on computer vision and machine learning for face recognition, object detection and region detection for medical applications. He has several granted patents. Sumedh earned a M.S. in Computer Science from U.T. Arlington and a B.S. in Biomedical/Medical Engineering from the B.M.S. College of Engineering.
Open for submissions! If you’re a start-up doing something cool with computer vision or visual AI, submit your entry today—for free! (Not at a computer vision or visual AI start-up but know one? Nominate them and you could win free Summit passes!) https://hubs.ly/Q01ws0cX0
Last chance! Don’t miss your chance to win year-round promotion of your company by the Alliance by winning an Edge AI and Vision Product of the Year Award. Submit your entry by December 31! https://hubs.ly/Q01svnzB0