Multiple Object Tracking (MOT) has rapidly progressed in recent years. Existing works tend to design a single tracking algorithm to perform both detection and association. Though ensemble learning has been exploited in many tasks, i.e, classification and object detection, it hasn't been studied in the MOT task, which is mainly caused by its complexity and evaluation metrics. In this paper, we propose a simple but effective ensemble method for MOT, called EnsembleMOT, which merges multiple tracking results from various trackers with spatio-temporal constraints. Meanwhile, several post-processing procedures are applied to filter out abnormal results. Our method is model-independent and doesn't need the learning procedure. What's more, it can easily work in conjunction with other algorithms, e.g., tracklets interpolation. Experiments on the MOT17 dataset demonstrate the effectiveness of the proposed method. Codes are available at https://github.com/dyhBUPT/EnsembleMOT.

翻译：多目标跟踪（MOT）近年来取得了快速发展。现有工作倾向于设计单一的跟踪算法来同时执行检测和关联。尽管集成学习已在分类、目标检测等多项任务中得到应用，但由于其复杂性和评估指标的限制，其在MOT任务中尚未得到研究。本文提出一种简单而有效的MOT集成方法——EnsembleMOT，该方法通过时空约束融合来自不同跟踪器的多个跟踪结果，同时采用若干后处理步骤过滤异常结果。我们的方法具有模型无关性，无需学习过程，且能轻松与其他算法（如轨迹插值）协同工作。在MOT17数据集上的实验证明了该方法的有效性。代码已开源：https://github.com/dyhBUPT/EnsembleMOT。