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. Code is available at https://github.com/feiaxyt/Winner_ECCV20_TAO.
翻译：我们将传统的逐项追踪模式扩展至此跟踪任务。 可靠的检测结果首先从 TAO 数据集中提取 。 一些最先进的技术, 如\ textbf{BA} lanced- textbf{G}rookup\ textbf{S} s}textbf{BAGS}}cite{li202020Rover}, 以及 探测器在检测过程中被整合。 然后我们通过培训功能学习网络学习了代表任何对象的外观特征。 我们共同使用几种模型来改进探测和特征代表。 与最相似的外观特征和跟踪级后级关联模块的简单连接策略最终被应用来产生最终跟踪结果。 我们的方法在挑战网站上以 \ textbf{AOA} 的形式提交 。 代码可在 https://github.com/ feiaxyaxyt/ Winner_ECCV20_TAO 上查阅 。
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