Visual Person Identification Using a Distance-dependent Appearance Model for a Person Following Robot
This paper describes a person identification method for a mobile robot which performs specific person following under dynamic complicated environments like a school canteen where many persons exist. We propose a distance-dependent appearance model which is based on scale-invariant feature transform (SIFT) feature. SIFT is a powerful image feature that is invariant to scale and rotation in the image plane and also robust to changes of lighting condition. However, the feature is weak against affine transformations and the identification power will thus be degraded when the pose of a person changes largely. We therefore use a set of images taken from various directions to cope with pose changes. Moreover, the number of SIFT feature matches between the model and an input image will decrease as the person becomes farther away from the camera. Therefore, we also use a distance-dependent threshold. The person following experiment was conducted using an actual mobile robot, and the quality assessment of person identification was performed.
identification mobile robots scale-invariant feature transform (sift) feature image processing intelligent systems