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。

Mesh-SORT: 简单有效的基于位置的跟踪器与遗失管理策略

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

Multi-object tracking is a fundamental vision problem that has been studied
for a long time. As deep learning brings excellent performances to object
detection algorithms, Tracking by Detection (TBD) has become the mainstream
tracking framework. Despite the success of TBD, this two-step method is too
complicated to train in an end-to-end manner and induces many challenges as
well, such as insufficient exploration of video spatial-temporal information,
vulnerability when facing object occlusion, and excessive reliance on detection
results. To address these challenges, we propose a concise end-to-end model
TubeTK which only needs one step training by introducing the ``bounding-tube"
to indicate temporal-spatial locations of objects in a short video clip. TubeTK
provides a novel direction of multi-object tracking, and we demonstrate its
potential to solve the above challenges without bells and whistles. We analyze
the performance of TubeTK on several MOT benchmarks and provide empirical
evidence to show that TubeTK has the ability to overcome occlusions to some
extent without any ancillary technologies like Re-ID. Compared with other
methods that adopt private detection results, our one-stage end-to-end model
achieves state-of-the-art performances even if it adopts no ready-made
detection results. We hope that the proposed TubeTK model can serve as a simple
but strong alternative for video-based MOT task. The code and models are
available at this https URL

本文提出了一种新型的多目标跟踪模型 TubeTK，通过引入 “bounding-tube” 指示对象在短视频剪辑中的时空位置来训练该模型，而无需进行复杂的两步式训练。该模型在多个 MOT 常用基准测试中表现优异，即使不使用预先准备的检测结果，也能达到同类方法的最高性能。