  A service robot serving safely and politely needs to track the surrounding
people robustly, especially for Tour-Guide Robot (TGR). However, existing
multi-object tracking (MOT) or multi-person tracking (MPT) methods are not
applicable to TGR for the following reasons: 1. lacking relevant large-scale
datasets; 2. lacking applicable metrics to evaluate trackers. In this work, we
target the visual perceptual tasks for TGR and present the TGRDB dataset, a
novel large-scale multi-person tracking dataset containing roughly 5.6 hours of
annotated videos and over 450 long-term trajectories. Besides, we propose a
more applicable metric to evaluate trackers using our dataset. As part of our
work, we present TGRMPT, a novel MPT system that incorporates information from
head shoulder and whole body, and achieves state-of-the-art performance. We
have released our codes and dataset in https://github.com/wenwenzju/TGRMPT.
