Data association is one of the key research in tracking-by-detection framework. Due to frequent interactions among targets, there are various relationships among trajectories in crowded scenes which leads to problems in data association, such as association ambiguity, association omission, etc. To handle these problems, we propose hypothesis-testing based tracking (HTBT) framework to build potential associations between target by constructing and testing hypotheses. In addition, a spatio-temporal interaction graph (STIG) model is introduced to describe the basic interaction patterns of trajectories and test the potential hypotheses. Based on network flow optimization, we formulate offline tracking as a MAP problem. Experimental results show that our tracking framework improves the robustness of tracklet association when detection failure occurs during tracking. On the public MOT16, MOT17 and MOT20 benchmark, our method achieves competitive results compared with other state-of-the-art methods.

中文翻译：

---

基于时空联合交互建模的基于假设检验的跟踪

数据关联是跟踪检测框架的关键研究之一。由于目标之间的频繁交互，拥挤场景中的轨迹之间存在各种关系，导致数据关联问题，例如关联模糊、关联遗漏等。为了解决这些问题，我们提出了基于假设检验的跟踪（HTBT）框架通过构建和检验假设来建立目标之间的潜在关联。此外，引入了时空交互图（STIG）模型来描述轨迹的基本交互模式并测试潜在的假设。基于网络流量优化，我们将离线跟踪制定为一个 MAP 问题。实验结果表明，当跟踪过程中发生检测失败时，我们的跟踪框架提高了轨迹关联的鲁棒性。在公开的 MOT16、MOT17 和 MOT20 基准测试中，与其他最先进的方法相比，我们的方法取得了有竞争力的结果。