  Multi-object tracking (MOT) is an essential task in the computer vision
field. With the fast development of deep learning technology in recent years,
MOT has achieved great improvement. However, some challenges still remain, such
as sensitiveness to occlusion, instability under different lighting conditions,
non-robustness to deformable objects, etc. To address such common challenges in
most of the existing trackers, in this paper, a tracklet booster algorithm is
proposed, which can be built upon any other tracker. The motivation is simple
and straightforward: split tracklets on potential ID-switch positions and then
connect multiple tracklets into one if they are from the same object. In other
words, the tracklet booster consists of two parts, i.e., Splitter and
Connector. First, an architecture with stacked temporal dilated convolution
blocks is employed for the splitting position prediction via label smoothing
strategy with adaptive Gaussian kernels. Then, a multi-head self-attention
based encoder is exploited for the tracklet embedding, which is further used to
connect tracklets into larger groups. We conduct sufficient experiments on
MOT17 and MOT20 benchmark datasets, which demonstrates promising results.
Combined with the proposed tracklet booster, existing trackers usually can
achieve large improvements on the IDF1 score, which shows the effectiveness of
the proposed method.
