Tech Forces Introduce New Retail Robot
Trax Image Recognition (Trax), the world leader in computer vision solutions for the retail industry and consumer packaged goods manufacturers, has announced its first partnership in the robotics industry. This development will combine advanced robotics technology and computer vision to provide store shelf analytics and insights for retailers, resulting in breakthrough operational savings.
Pioneering robotics company Fetch Robotics will integrate their autonomous mobile platform robot called Freight, with Trax’s image recognition platform. The combination of Trax and Freight will enable retailers to capture shelf images more effectively using autonomous robots, and convert that data into actionable, real-time insights.
Trax will be demonstrating the capabilities of its image recognition platform and Fetch robots at this year’s NRF The Big Retail Event during 15-17 January, New York.
Retailers already monitor their shelf activity, but to date it has been completed in labor intensive and fragmented ways, resulting in out of date, innaccurate and inconsistent information. By integrating Trax and Freight, retailers will be able to reduce operational costs, increase revenues, and secure additional analytics and insights about the shelf to support both immediate and future in-store strategies.
“The technologies retailers are adopting today are rapidly innovating, and the advancement of computer vision technologies is pushing those boundaries even further. Applications that were once thought of as sci-fi are now a reality for retailers, providing a whole new level of insight and analytics,” said Steve Hornyak, Americas CEO at Trax. “This next step for the industry will significantly decrease operational overheads and make retailers smarter and more agile, all of which lead to increased revenues.”
“We are pleased to partner with Trax and create a retail-optimized robot combining Trax’s image recognition solution with our autonomous robot platform," added Melonee Wise, CEO of Fetch Robotics. "By combining our expertise in robotics with Trax’s retail experience, we have a great opportunity to solve challenges that have been troubling the retail industry for a long time.”
The Fetch Robotics datasurvey solution empowers partners like Trax to automate and mobilize data gathering tasks. The Fetch shelfsurveyor mobile camera robot and fetchcore software provide safe and precise movement of the camera array through retail environments to ensure accurate and efficient image capture. This highly extensible physical architecture seamlessly integrates with the Trax image recognition platform, providing an overall solution that is more accurate and cost efficient.
Using all types of data gathering technologies including smart-phones, tablets, fixed cameras, and now robotics, Trax turns shelf images into real-time actionable insights through its unique computer vision platform. It’s advanced image recognition engine has recognized more than 6 billion images. Trax is leading the market with over 1 million store visits and over a quarter billion images recognized and converted to usable analytic insights each month. By using innovative and sophisticated technologies and techniques, Trax provides the highest accuracy of data, intelligence, insights and recommendations to consumer good companies and retailers in more than 45 countries.
Steve continued: “The marriage of advanced robotics and computer vision technologies is a game changer for the industry – decreasing operational overhead, providing better customer experiences and allowing physical retailers to better combat the onslaught of ecommerce. As the world leader of computer vision solutions for retail and consumer packaged goods companies, we are well on our way to becoming the computer vision platform of choice for all image gathering technologies.”
Trax, as well as Fetch’s ‘Powered by Trax’ robot, will be exhibiting at NRF’s The Big Retail Event (15-17 January). Trax’s booth #4355 , Intel’s booth #3125 Innovation Lab #RP29 during the Robotics & AI sessions (January 15, 10.15 – 11.15am or January 16, 12.25 – 1.25pm)
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