  The one-shot multi-object tracking, which integrates object detection and ID
embedding extraction into a unified network, has achieved groundbreaking
results in recent years. However, current one-shot trackers solely rely on
single-frame detections to predict candidate bounding boxes, which may be
unreliable when facing disastrous visual degradation, e.g., motion blur,
occlusions. Once a target bounding box is mistakenly classified as background
by the detector, the temporal consistency of its corresponding tracklet will be
no longer maintained. In this paper, we set out to restore the bounding boxes
misclassified as ``fake background'' by proposing a re-check network. The
re-check network innovatively expands the role of ID embedding from data
association to motion forecasting by effectively propagating previous tracklets
to the current frame with a small overhead. Note that the propagation results
are yielded by an independent and efficient embedding search, preventing the
model from over-relying on detection results. Eventually, it helps to reload
the ``fake background'' and repair the broken tracklets. Building on a strong
baseline CSTrack, we construct a new one-shot tracker and achieve favorable
gains by 70.7 $\rightarrow$ 76.4, 70.6 $\rightarrow$ 76.3 MOTA on MOT16 and
MOT17, respectively. It also reaches a new state-of-the-art MOTA and IDF1
performance. Code is released at https://github.com/JudasDie/SOTS.
