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.

翻译：多目标跟踪（MOT）因其在交通与行人检测中的潜在应用近年来受到广泛关注。我们注意到基于检测的跟踪可能受到噪声检测器产生的误差影响（例如遮挡前不精确的边界框），并观察到在大多数跟踪场景中，物体倾向于在特定位置移动和丢失。针对这一问题，我们提出一种新型跟踪器来处理不良检测器与遮挡。首先，我们提出一种位置感知子区域识别方法，将画面等分为网格（mesh）。随后提出相应的位置感知丢失管理策略及不同匹配策略。由此产生的Mesh-SORT消融实验表明，与MOT17数据集上的基线相比，该方法使碎片化减少3%、身份切换减少7.2%，MOTA提升0.4%。最后，我们分析了该方法在特定场景下的局限性，并探讨了未来可扩展的研究方向。