In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input in video. The proposed method exploits the Gaussian mixture probability hypothesis density (GMPHD) filter for online approach which is extended with a hierarchical data association (HDA) and a simple affinity fusion (SAF) model. HDA consists of segment-to-track and track-to-track associations. To build the SAF model, an affinity is computed by using the GMPHD filter that is represented by the Gaussian mixture models with position and motion mean vectors, and another affinity for appearance is computed by using the responses from single object tracker such as the kernalized correlation filters. These two affinities are simply fused by using a score-level fusion method such as Min-max normalization. In addition, to reduce false positive segments, we adopt Mask IoU based merging. In experiments, those key modules, i.e., HDA, SAF, and Mask merging show incremental improvements. For instance, ID-switch decreases by half compared to baseline method. In conclusion, our tracker achieves state-of-the-art level MOTS performance.

中文翻译：

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使用 GMPHD 过滤器和 Simple Affinity Fusion 进行在线多对象跟踪和分割

在本文中，我们提出了一种高度实用的完全在线多对象跟踪和分割（MOTS）方法，该方法使用实例分割结果作为视频的输入。所提出的方法利用高斯混合概率假设密度 (GMPHD) 滤波器进行在线方法，该方法扩展了分层数据关联 (HDA) 和简单的亲和融合 (SAF) 模型。HDA 由段到轨道和轨道到轨道关联组成。为了构建 SAF 模型，使用由具有位置和运动均值向量的高斯混合模型表示的 GMPHD 滤波器计算亲和力，并通过使用来自单个对象跟踪器的响应（例如核化相关性）计算外观的另一个亲和力过滤器。这两个相似性通过使用分数级融合方法（例如 Min-max 归一化）简单地融合。此外，为了减少误报段，我们采用基于 Mask IoU 的合并。在实验中，这些关键模块，即 HDA、SAF 和 Mask 合并显示出增量改进。例如，与基线方法相比，ID-switch 减少了一半。总之，我们的跟踪器实现了最先进的 MOTS 性能。