  Recent works in multiple object tracking use sequence model to calculate the
similarity score between the detections and the previous tracklets. However,
the forced exposure to ground-truth in the training stage leads to the
training-inference discrepancy problem, i.e., exposure bias, where association
error could accumulate in the inference and make the trajectories drift. In
this paper, we propose a novel method for optimizing tracklet consistency,
which directly takes the prediction errors into account by introducing an
online, end-to-end tracklet search training process. Notably, our methods
directly optimize the whole tracklet score instead of pairwise affinity. With
sequence model as appearance encoders of tracklet, our tracker achieves
remarkable performance gain from conventional tracklet association baseline.
Our methods have also achieved state-of-the-art in MOT15~17 challenge
benchmarks using public detection and online settings.
