A SIFT-Based person identification using a distance-dependent appearance model for a person following robot

Abstract

This paper describes a person identification technique for a mobile robot which performs specific person following under dynamic complicated environments like a school canteen where many persons exist. We use the SIFT feature for identification of a person, and create the distance dependence appearance model which expects the number of SIFT feature matches based on the distance to a person. The person following experiment was conducted using an actual mobile robot, and the quality assessment of person identification was performed.