  We propose a post-processor, called NeighborTrack, that leverages neighbor
information of the tracking target to validate and improve single-object
tracking (SOT) results. It requires no additional data or retraining. Instead,
it uses the confidence score predicted by the backbone SOT network to
automatically derive neighbor information and then uses this information to
improve the tracking results. When tracking an occluded target, its appearance
features are untrustworthy. However, a general siamese network often cannot
tell whether the tracked object is occluded by reading the confidence score
alone, because it could be misled by neighbors with high confidence scores. Our
proposed NeighborTrack takes advantage of unoccluded neighbors' information to
reconfirm the tracking target and reduces false tracking when the target is
occluded. It not only reduces the impact caused by occlusion, but also fixes
tracking problems caused by object appearance changes. NeighborTrack is
agnostic to SOT networks and post-processing methods. For the VOT challenge
dataset commonly used in short-term object tracking, we improve three famous
SOT networks, Ocean, TransT, and OSTrack, by an average of ${1.92\%}$ EAO and
${2.11\%}$ robustness. For the mid- and long-term tracking experiments based on
OSTrack, we achieve state-of-the-art ${72.25\%}$ AUC on LaSOT and ${75.7\%}$ AO
on GOT-10K. Code duplication can be found in
https://github.com/franktpmvu/NeighborTrack.
