We consider the tracking problem as a special type of object detection
problem, which we call instance detection. With proper initialization, a
detector can be quickly converted into a tracker by learning the new instance
from a single image. We find that model-agnostic meta-learning (MAML) offers a
strategy to initialize the detector that satisfies our needs. We propose a
principled three-step approach to build a high-performance tracker. First, pick
any modern object detector trained with gradient descent. Second, conduct
offline training (or initialization) with MAML. Third, perform domain
adaptation using the initial frame. We follow this procedure to build two
trackers, named Retina-MAML and FCOS-MAML, based on two modern detectors
RetinaNet and FCOS. Evaluations on four benchmarks show that both trackers are
competitive against state-of-the-art trackers. On OTB-100, Retina-MAML achieves
the highest ever AUC of 0.712. On TrackingNet, FCOS-MAML ranks the first on the
leader board with an AUC of 0.757 and the normalized precision of 0.822. Both
trackers run in real-time at 40 FPS.

本文提供了一种将目标检测器（如 RetinaNet 和 FCOS）快速转换为跟踪器的方法，即使用元学习方法（MAML）进行离线训练和领域自适应，并展示了通过该方法构建的 Retina-MAML 和 FCOS-MAML 跟踪器在四个基准测试中的竞争力。