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A service robot serving safely and politely needs to track the surroundingpeople robustly, especially for Tour-Guide Robot (TGR). However, existingmulti-object tracking (MOT) or multi-person tracking (MPT) methods are notapplicable to TGR for the following reasons: 1. lacking relevant large-scaledatasets; 2. lacking applicable metrics to evaluate trackers. In this work, wetarget the visual perceptual tasks for TGR and present the TGRDB dataset, anovel large-scale multi-person tracking dataset containing roughly 5.6 hours ofannotated videos and over 450 long-term trajectories. Besides, we propose amore applicable metric to evaluate trackers using our dataset. As part of ourwork, we present TGRMPT, a novel MPT system that incorporates information fromhead shoulder and whole body, and achieves state-of-the-art performance. Wehave released our codes and dataset in https://github.com/wenwenzju/TGRMPT.

TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New Large-Scale Dataset for Tour-Guide Robot