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Traditional multiple object tracking methods divide the task into two parts:affinity learning and data association. The separation of the task requires todefine a hand-crafted training goal in affinity learning stage and ahand-crafted cost function of data association stage, which prevents thetracking goals from learning directly from the feature. In this paper, wepresent a new multiple object tracking (MOT) framework with data-drivenassociation method, named as Tracklet Association Tracker (TAT). The frameworkaims at gluing feature learning and data association into a unity by a bi-leveloptimization formulation so that the association results can be directlylearned from features. To boost the performance, we also adopt the popularhierarchical association and perform the necessary alignment and selection ofraw detection responses. Our model trains over 20X faster than a similarapproach, and achieves the state-of-the-art performance on both MOT2016 andMOT2017 benchmarks.

Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking