论文标题
视网膜：在线单阶段联合检测和跟踪
RetinaTrack: Online Single Stage Joint Detection and Tracking
论文作者
论文摘要
传统上，使用单独的系统，大多数先前的作品专门针对另一个方面，而与另一个方面相比，多数先前的作品进行了多个目标跟踪和对象检测。但是，跟踪系统显然受益于获得准确的检测，但是文献中有足够的证据表明，检测器可以从跟踪中受益，例如，这种跟踪可以帮助随着时间的流逝而有助于平稳的预测。在本文中，我们专注于自动驾驶的逐个追踪范式，这两个任务都至关重要。我们提出了一种概念上简单有效的检测和跟踪联合模型，称为Retinatrack，该模型修改了流行的单阶段视网膜方法，以便可以容纳实例级级嵌入训练。通过对Waymo Open数据集的评估，我们表明，我们的表现要优于最新的最新跟踪算法状态，同时需要大大减少计算。我们认为，我们简单而有效的方法可以成为该领域未来工作的强大基准。
Traditionally multi-object tracking and object detection are performed using separate systems with most prior works focusing exclusively on one of these aspects over the other. Tracking systems clearly benefit from having access to accurate detections, however and there is ample evidence in literature that detectors can benefit from tracking which, for example, can help to smooth predictions over time. In this paper we focus on the tracking-by-detection paradigm for autonomous driving where both tasks are mission critical. We propose a conceptually simple and efficient joint model of detection and tracking, called RetinaTrack, which modifies the popular single stage RetinaNet approach such that it is amenable to instance-level embedding training. We show, via evaluations on the Waymo Open Dataset, that we outperform a recent state of the art tracking algorithm while requiring significantly less computation. We believe that our simple yet effective approach can serve as a strong baseline for future work in this area.
