  In this paper, we focus on improving online multi-object tracking (MOT). In
particular, we introduce a region-based Siamese Multi-Object Tracking network,
which we name SiamMOT. SiamMOT includes a motion model that estimates the
instance's movement between two frames such that detected instances are
associated. To explore how the motion modelling affects its tracking
capability, we present two variants of Siamese tracker, one that implicitly
models motion and one that models it explicitly. We carry out extensive
quantitative experiments on three different MOT datasets: MOT17, TAO-person and
Caltech Roadside Pedestrians, showing the importance of motion modelling for
MOT and the ability of SiamMOT to substantially outperform the
state-of-the-art. Finally, SiamMOT also outperforms the winners of ACM MM'20
HiEve Grand Challenge on HiEve dataset. Moreover, SiamMOT is efficient, and it
runs at 17 FPS for 720P videos on a single modern GPU. Codes are available in
\url{https://github.com/amazon-research/siam-mot}.
