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.

本研究提出了一种基于子区域识别及匹配策略的 Mesh-SORT 多目标跟踪器，在 MOT17 数据集中相比基线实现了 3% 的分裂、7.2% 的标识符切换和 0.4% 的 MOTA 改进，适用于跟踪目标包括在复杂环境中的场景。