  Current multi-object tracking (MOT) aims to predict trajectories of targets
(i.e., ''where'') in videos. Yet, knowing merely ''where'' is insufficient in
many crucial applications. In comparison, semantic understanding such as
fine-grained behaviors, interactions, and overall summarized captions (i.e.,
''what'') from videos, associated with ''where'', is highly-desired for
comprehensive video analysis. Thus motivated, we introduce Semantic
Multi-Object Tracking (SMOT), that aims to estimate object trajectories and
meanwhile understand semantic details of associated trajectories including
instance captions, instance interactions, and overall video captions,
integrating ''where'' and ''what'' for tracking. In order to foster the
exploration of SMOT, we propose BenSMOT, a large-scale Benchmark for Semantic
MOT. Specifically, BenSMOT comprises 3,292 videos with 151K frames, covering
various scenarios for semantic tracking of humans. BenSMOT provides annotations
for the trajectories of targets, along with associated instance captions in
natural language, instance interactions, and overall caption for each video
sequence. To our best knowledge, BenSMOT is the first publicly available
benchmark for SMOT. Besides, to encourage future research, we present a novel
tracker named SMOTer, which is specially designed and end-to-end trained for
SMOT, showing promising performance. By releasing BenSMOT, we expect to go
beyond conventional MOT by predicting ''where'' and ''what'' for SMOT, opening
up a new direction in tracking for video understanding. We will release BenSMOT
and SMOTer at https://github.com/Nathan-Li123/SMOTer.
