Technologies that allow autonomous robots and computer systems to quickly recognize and interact with individuals in a group setting has the potential to enable a wide range of personalized experiences. However, existing solutions fail to both identify and locate individuals with enough speed to enable seamless interactions in very dynamic environments that require fast, implicit, non-intrusive, and ubiquitous recognition of users. In this work, we present a hybrid computer vision and RFID system that uses a novel reverse synthetic aperture technique to recover the relative motion paths of an RFID tags worn by people and correlate that to physical motion paths of individuals as measured with a 3D depth camera. Results show that our real-time system is capable of simultaneously recognizing and correctly assigning IDs to individuals within 4 seconds with 96.6% accuracy and groups of five people in 7 seconds with 95% accuracy. In order to test the effectiveness of this approach in realistic scenarios, groups of five participants play an interactive quiz game with an autonomous robot, resulting in an ID assignment accuracy of 93.3%.
['Hanchuan Li', 'Peijin Zhang', 'Samer Al Moubayed', 'Shwetak N. Patel', 'Alanson P. Sample']
ID-Match: A Hybrid Computer Vision and RFID System for Recognizing Individuals in Groups