Identification of a specific person using color, height, and gait features for a person following robot

This paper describes a person identification method for mobile service robots using image and range data. Person identification is a necessary function in order for mobile service robots to locate the target person for those services. Among various sensory features, image-based appearance features have often been used for person identification. They are, however, not effective in severe illumination environments such as a strong backlight. Therefore, we use two illumination-independent features, height and gait, in addition to appearance features for a more robust identification. To this end, we have developed a new method of extracting the gait feature (step length and speed), based on a maximum likelihood estimation of supporting leg positions in accumulated range data. We combine these features and use an online boosting approach to create the specific person classifier. It allows the robot to identify the specific person robustly even in a severe illumination environment. We tested our multi-feature person identification method, combined with a range data-based person tracker, in a specific person following scenario to demonstrate the effectiveness of this method. A multi-feature person identification method for mobile robots is proposed.The method adopts gait, height, and color features for robust identification.It is applicable to severe illumination conditions.Gait and height estimation methods for mobile robots are proposed.

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