  Current popular online multi-object tracking (MOT) solutions apply single
object trackers (SOTs) to capture object motions, while often requiring an
extra affinity network to associate objects, especially for the occluded ones.
This brings extra computational overhead due to repetitive feature extraction
for SOT and affinity computation. Meanwhile, the model size of the
sophisticated affinity network is usually non-trivial. In this paper, we
propose a novel MOT framework that unifies object motion and affinity model
into a single network, named UMA, in order to learn a compact feature that is
discriminative for both object motion and affinity measure. In particular, UMA
integrates single object tracking and metric learning into a unified triplet
network by means of multi-task learning. Such design brings advantages of
improved computation efficiency, low memory requirement and simplified training
procedure. In addition, we equip our model with a task-specific attention
module, which is used to boost task-aware feature learning. The proposed UMA
can be easily trained end-to-end, and is elegant - requiring only one training
stage. Experimental results show that it achieves promising performance on
several MOT Challenge benchmarks.
