  Traditional multiple object tracking methods divide the task into two parts:
affinity learning and data association. The separation of the task requires to
define a hand-crafted training goal in affinity learning stage and a
hand-crafted cost function of data association stage, which prevents the
tracking goals from learning directly from the feature. In this paper, we
present a new multiple object tracking (MOT) framework with data-driven
association method, named as Tracklet Association Tracker (TAT). The framework
aims at gluing feature learning and data association into a unity by a bi-level
optimization formulation so that the association results can be directly
learned from features. To boost the performance, we also adopt the popular
hierarchical association and perform the necessary alignment and selection of
raw detection responses. Our model trains over 20X faster than a similar
approach, and achieves the state-of-the-art performance on both MOT2016 and
MOT2017 benchmarks.
