  In recent years, anchor-free object detection models combined with matching
algorithms are used to achieve real-time muti-object tracking and also ensure
high tracking accuracy. However, there are still great challenges in
multi-object tracking. For example, when most part of a target is occluded or
the target just disappears from images temporarily, it often leads to tracking
interruptions for most of the existing tracking algorithms. Therefore, this
study offers a bi-directional matching algorithm for multi-object tracking that
makes advantage of bi-directional motion prediction information to improve
occlusion handling. A stranded area is used in the matching algorithm to
temporarily store the objects that fail to be tracked. When objects recover
from occlusions, our method will first try to match them with objects in the
stranded area to avoid erroneously generating new identities, thus forming a
more continuous trajectory. Experiments show that our approach can improve the
multi-object tracking performance in the presence of occlusions. In addition,
this study provides an attentional up-sampling module that not only assures
tracking accuracy but also accelerates training speed. In the MOT17 challenge,
the proposed algorithm achieves 63.4% MOTA, 55.3% IDF1, and 20.1 FPS tracking
speed.
