论文标题
统一的变压器跟踪器用于对象跟踪
Unified Transformer Tracker for Object Tracking
论文作者
论文摘要
作为计算机视觉的重要领域，对象跟踪形成了两个独立的社区，分别研究单个对象跟踪（SOT）和多个对象跟踪（MOT）。但是，由于两个任务的培训数据集和跟踪对象，当前方法在一个跟踪方案中不容易适应另一种方法。尽管unitrack \ cite {wang2021Diverent}表明，具有多个头部的共享外观模型可用于处理单个跟踪任务，但它无法利用大规模跟踪数据集进行训练，并且在单个对象跟踪上执行良好的训练。在这项工作中，我们提出了统一的变压器跟踪器（UTT），以通过一个范式在不同情况下解决跟踪问题。在我们的UTT中开发了轨道变压器，以跟踪SOT和MOT中的目标。利用目标和跟踪框架特征之间的相关性来定位目标。我们证明SOT和MOT任务都可以在此框架内解决。该模型可以同时通过在单个任务的数据集中优化SOT和MOT目标，同时端到端训练。在几个基准测试基准上进行了广泛的实验，并具有在SOT和MOT数据集上训练的统一模型。代码将在https://github.com/flowerfan/trackron上找到。
As an important area in computer vision, object tracking has formed two separate communities that respectively study Single Object Tracking (SOT) and Multiple Object Tracking (MOT). However, current methods in one tracking scenario are not easily adapted to the other due to the divergent training datasets and tracking objects of both tasks. Although UniTrack \cite{wang2021different} demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on single object tracking. In this work, we present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both SOT and MOT. The correlation between the target and tracking frame features is exploited to localize the target. We demonstrate that both SOT and MOT tasks can be solved within this framework. The model can be simultaneously end-to-end trained by alternatively optimizing the SOT and MOT objectives on the datasets of individual tasks. Extensive experiments are conducted on several benchmarks with a unified model trained on SOT and MOT datasets. Code will be available at https://github.com/Flowerfan/Trackron.
