Stereo Vision-Based Human Tracking for Robotic Follower

Abstract

Abstract This paper addresses the problem of real-time vision-based human tracking to enable mobile robots to follow a human co-worker. A novel approach to combine stereo vision-based human detection with human tracking using a modified Kalman filter is presented. Stereo vision-based detection combines features extracted from 2D stereo images with reconstructed 3D object features to detect humans in a robot's environment. For human tracking a modified Kalman filter recursively predicts and updates estimates of the 3D coordinates of a human in the robot's camera coordinate system. This prediction enables human detection to be performed on the image region of interest contributing to cost effective human tracking. The performance of the presented method was tested within a working scenario of a mobile robot intended to follow a human co-worker in indoor applications as well as in outdoor applications.