Real-Time Pose Based Human Detection and Re-Identification with a Single Camera for Robot Person Following

Abstract

In this work we address the challenge of following a person with a mobile

robot, with a focus on the image processing aspect. We overview different historical

approaches for person following and outline the advantages and disadvantages of

each. We then show that recent convolutional neural networks trained for human

pose detection are suitable for person detection as it relates to the robot following

problem. We extend one such pose detection network to spatially embed the identity

of individuals in the image, utilizing the pose features already computed. The

proposed identity embedding allows the system to robustly track individuals in

consecutive frames even in long term occlusion or absence. The final system provides

a robust person tracking scheme which is suitable for person following.