  Tracking multiple objects in videos relies on modeling the spatial-temporal
interactions of the objects. In this paper, we propose a solution named
TransMOT, which leverages powerful graph transformers to efficiently model the
spatial and temporal interactions among the objects. TransMOT effectively
models the interactions of a large number of objects by arranging the
trajectories of the tracked objects as a set of sparse weighted graphs, and
constructing a spatial graph transformer encoder layer, a temporal transformer
encoder layer, and a spatial graph transformer decoder layer based on the
graphs. TransMOT is not only more computationally efficient than the
traditional Transformer, but it also achieves better tracking accuracy. To
further improve the tracking speed and accuracy, we propose a cascade
association framework to handle low-score detections and long-term occlusions
that require large computational resources to model in TransMOT. The proposed
method is evaluated on multiple benchmark datasets including MOT15, MOT16,
MOT17, and MOT20, and it achieves state-of-the-art performance on all the
datasets.
