Autonomous robot human detecting and tracking based on stereo vision

Abstract

Detecting and tracking people are challenging problems, because the human body is non-rigid and the detected human are easily occluded by other objects. In this paper, we present a robust human detecting and tracking system which can be used in indoor environments. The proposed method is to get the human's disparity image from stereo camera, and then we extract the identify model human of by image processing. Hu moment is chosen to detect human because it has the invariant character of translation, rotation, proportion. Robust human tracking is performed with Extend Kalman Filter (EKF) as it's flexible and easy to apply in practical environments. In proposed system, the operator can monitor the process; modify the parameters and observe the experiment results in developed interactive GUI. Our experiments have demonstrated that the method improves human detecting and tracking robustness.