  Tracking by detection paradigm is one of the most popular object tracking
methods. However, it is very dependent on the performance of the detector. When
the detector has a behavior of missing detection, the tracking result will be
directly affected. In this paper, we analyze the phenomenon of the lost
tracking object in real-time tracking model on MOT2020 dataset. Based on simple
and traditional methods, we propose a compensation tracker to further alleviate
the lost tracking problem caused by missing detection. It consists of a motion
compensation module and an object selection module. The proposed method not
only can re-track missing tracking objects from lost objects, but also does not
require additional networks so as to maintain speed-accuracy trade-off of the
real-time model. Our method only needs to be embedded into the tracker to work
without re-training the network. Experiments show that the compensation tracker
can efficaciously improve the performance of the model and reduce identity
switches. With limited costs, the compensation tracker successfully enhances
the baseline tracking performance by a large margin and reaches 66% of MOTA and
67% of IDF1 on MOT2020 dataset.
