  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no longer maintained, as shown in Fig. 1. In this paper, we set out to restore
the misclassified bounding boxes, i.e., fake background, by proposing a
re-check network. The re-check network propagates previous tracklets to the
current frame by exploring the relation between cross-frame temporal cues and
current candidates using the modified cross-correlation layer. The propagation
results help to reload the "fake background" and eventually repair the broken
tracklets. By inserting the re-check network to a strong baseline tracker
CSTrack (a variant of JDE), our model achieves favorable gains by $70.7
\rightarrow 76.7$, $70.6 \rightarrow 76.3$ MOTA on MOT16 and MOT17,
respectively. Code is publicly available at https://github.com/JudasDie/SOTS.
