  Modern online multiple object tracking (MOT) methods usually focus on two
directions to improve tracking performance. One is to predict new positions in
an incoming frame based on tracking information from previous frames, and the
other is to enhance data association by generating more discriminative identity
embeddings. Some works combined both directions within one framework but
handled them as two individual tasks, thus gaining little mutual benefits. In
this paper, we propose a novel unified model with synergy between position
prediction and embedding association. The two tasks are linked by
temporal-aware target attention and distractor attention, as well as
identity-aware memory aggregation model. Specifically, the attention modules
can make the prediction focus more on targets and less on distractors,
therefore more reliable embeddings can be extracted accordingly for
association. On the other hand, such reliable embeddings can boost
identity-awareness through memory aggregation, hence strengthen attention
modules and suppress drifts. In this way, the synergy between position
prediction and embedding association is achieved, which leads to strong
robustness to occlusions. Extensive experiments demonstrate the superiority of
our proposed model over a wide range of existing methods on MOTChallenge
benchmarks. Our code and models are publicly available at
https://github.com/songguocode/TADAM.
