  Multi-Object Tracking (MOT) has gained extensive attention in recent years
due to its potential applications in traffic and pedestrian detection. We note
that tracking by detection may suffer from errors generated by noise detectors,
such as an imprecise bounding box before the occlusions, and observed that in
most tracking scenarios, objects tend to move and lost within specific
locations. To counter this, we present a novel tracker to deal with the bad
detector and occlusions. Firstly, we proposed a location-wise sub-region
recognition method which equally divided the frame, which we called mesh. Then
we proposed corresponding location-wise loss management strategies and
different matching strategies. The resulting Mesh-SORT, ablation studies
demonstrate its effectiveness and made 3% fragmentation 7.2% ID switches drop
and 0.4% MOTA improvement compared to the baseline on MOT17 datasets. Finally,
we analyze its limitation on the specific scene and discussed what future works
can be extended.
