论文标题
通过不确定性流进行健壮的实例跟踪
Robust Instance Tracking via Uncertainty Flow
论文作者
论文摘要
当前的最新跟踪器通常由于干扰物和大对象外观的变化而失败。在这项工作中，我们探讨了密度光流的使用以改善跟踪鲁棒性。我们的主要见解是，由于流量估计也可能存在错误，因此我们需要结合稳健跟踪的流动不确定性的估计值。我们提出了一个新颖的跟踪框架，该框架结合了外观和流动不确定性信息，以在充满挑战的情况下跟踪对象。我们通过实验验证我们的框架是否可以改善跟踪鲁棒性，从而导致新的最新结果。此外，我们的实验消融表明，流动不确定性对于鲁棒跟踪的重要性。
Current state-of-the-art trackers often fail due to distractorsand large object appearance changes. In this work, we explore the use ofdense optical flow to improve tracking robustness. Our main insight is that, because flow estimation can also have errors, we need to incorporate an estimate of flow uncertainty for robust tracking. We present a novel tracking framework which combines appearance and flow uncertainty information to track objects in challenging scenarios. We experimentally verify that our framework improves tracking robustness, leading to new state-of-the-art results. Further, our experimental ablations shows the importance of flow uncertainty for robust tracking.
