论文标题

通过实例检测跟踪:一种元学习方法

Tracking by Instance Detection: A Meta-Learning Approach

论文作者

Wang, Guangting, Luo, Chong, Sun, Xiaoyan, Xiong, Zhiwei, Zeng, Wenjun

论文摘要

我们将跟踪问题视为一种特殊类型的对象检测问题,我们调用实例检测。通过适当的初始化,可以通过从单个图像中学习新实例来快速转换为跟踪器。我们发现,模型不足的元学习(MAML)提供了一种策略来初始化满足我们需求的检测器。我们提出了一种有原则的三步方法来构建高性能跟踪器。首先,选择任何接受梯度下降训练的现代对象探测器。其次,用MAML进行离线训练(或初始化)。第三,使用初始帧执行域适应。我们遵循此程序,根据两个现代探测器视网膜和FCO构建两个名为Retina-MAML和FCOS-MAML的跟踪器。对四个基准测试的评估表明,这两个跟踪器都与最先进的跟踪器竞争。在OTB-100上,Retina-MAML的AUC最高为0.712。在TrackingNet上,FCOS-MAML以AUC为0.757,标准化精度为0.822,将排名第一。两个跟踪器以40 fps实时运行。

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.

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