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 \textit{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.

翻译：跟踪(MPF)的单体人能力支持移动机器人的许多有用应用。 但是,现有的 MPF 解决方案并不完全令人满意。 首先,它们往往无法在近距离跟踪目标,因为它们基于视觉瑟沃或需要机器人对全身进行观察。 其次,在目标外观变化的情况下,其目标再识别(再识别)能力薄弱,而且转移人的外观也非常相似。为了消除对全体观察的假设,我们提议了一个基于目标宽度的跟踪模块,该模块甚至可以在近距离观测。在处理与外观变化有关的问题时,我们使用全球CNN(革命神经网络)描述仪来代表目标,并使用脊椎回归模型在网上学习目标外观模型。我们采用了一个用于在线分类学习的抽样战略,其中涉及长期和短期的样本。我们用两个数据集来评估我们的方法,包括跟踪数据集的公众和定制的、具有挑战性外观和目标距离的定制的模块。为了在公共源代码上取得数据结果。