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.

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This paper describes a stereo-based person detection and tracking method for a mobile robot that can follow a specific person in dynamic environments. Many previous works on person detection use laser range finders which can provide very accurate range measurements. Stereo-based systems have also been popular, but most of them have not been used for controlling a real robot. We propose a detection method using depth templates of person shape applied to a dense depth image. We also develop an SVM-based verifier for eliminating false positive. For person tracking by a mobile platform, we formulate the tracking problem using the Extended Kalman filter. The robot continuously estimates the position and the velocity of persons in the robot local coordinates, which are then used for appropriately controlling the robot motion. Although our approach is relatively simple, our robot can robustly follow a specific person while recognizing the target and other persons with occasional occlusions.

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