  The problem of multi-object tracking is a fundamental computer vision
research focus, widely used in public safety, transport, autonomous vehicles,
robotics, and other regions involving artificial intelligence. Because of the
complexity of natural scenes, object occlusion and semi-occlusion usually occur
in fundamental tracking tasks. These can easily lead to ID switching, object
loss, detect errors, and misaligned limitation boxes. These conditions have a
significant impact on the precision of multi-object tracking. In this paper, we
design a new multi-object tracker for the above issues that contains an object
\textbf{Relative Location Mapping} (RLM) model and \textbf{Target Region
Density} (TRD) model. The new tracker is more sensitive to the differences in
position relationships between objects. It can introduce low-score detection
frames into different regions in real-time according to the density of object
regions in the video. This improves the accuracy of object tracking without
consuming extensive arithmetic resources. Our study shows that the proposed
model has considerably enhanced the HOTA and DF1 measurements on the MOT17 and
MOT20 data sets when applied to the advanced MOT method.
