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

Mesh-SORT: Simple and effective location-wise tracker with lost management strategies