We extend the classical tracking-by-detection paradigm to this tracking-any-object task. Solid detection results are first extracted from TAO dataset. Some state-of-the-art techniques like \textbf{BA}lanced-\textbf{G}roup \textbf{S}oftmax (\textbf{BAGS}\cite{li2020overcoming}) and DetectoRS\cite{qiao2020detectors} are integrated during detection. Then we learned appearance features to represent any object by training feature learning networks. We ensemble several models for improving detection and feature representation. Simple linking strategies with most similar appearance features and tracklet-level post association module are finally applied to generate final tracking results. Our method is submitted as \textbf{AOA} on the challenge website.

中文翻译：

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ECCV-TAO-2020的第一名解决方案：检测并表示任何对象以进行跟踪

我们将经典的“按检测跟踪”范式扩展到此“跟踪任何对象”任务。首先从TAO数据集中提取固体检测结果。集成了一些最新技术，例如\ textbf {BA} lanced- \ textbf {G} roup \ textbf {S} oftmax（\ textbf {BAGS} \ cite {li2020overcoming}）和DetectoRS \ cite {qiao2020detectors}在检测期间。然后，我们通过训练特征学习网络学习了外观特征来表示任何对象。我们集成了几个模型来改进检测和特征表示。具有最相似外观特征的简单链接策略和Tracklet级帖子关联模块最终被应用以生成最终的跟踪结果。我们的方法在挑战网站上以\ textbf {AOA}的形式提交。