  Monocular person following (MPF) is a capability that supports many useful
applications of a mobile robot. However, existing MPF solutions are not
completely satisfactory. Firstly, they often fail to track the target at a
close distance either because they are based on a visual servo or they need the
observation of the full body by the robot. Secondly, their target
Re-IDentification (Re-ID) abilities are weak in cases of target appearance
change and highly similar appearance of distracting people. To remove the
assumption of full-body observation, we propose a width-based tracking module,
which relies on the target width, which can be observed even at a close
distance. For handling issues related to appearance variation, we use a global
CNN (convolutional neural network) descriptor to represent the target and a
ridge regression model to learn a target appearance model online. We adopt a
sampling strategy for online classifier learning, in which both long-term and
short-term samples are involved. We evaluate our method in two datasets
including a public person following dataset and a custom-built one with
challenging target appearance and target distance. Our method achieves
state-of-the-art (SOTA) results on both datasets. For the benefit of the
community, we make public the dataset and the source code.
