We introduce a novel motion estimation method, MaskFlow, that is capable of
estimating accurate motion fields, even in very challenging cases with small
objects, large displacements and drastic appearance changes. In addition to
lower-level features, that are used in other Deep Neural Network (DNN)-based
motion estimation methods, MaskFlow draws from object-level features and
segmentations. These features and segmentations are used to approximate the
objects' translation motion field. We propose a novel and effective way of
incorporating the incomplete translation motion field into a subsequent motion
estimation network for refinement and completion. We also produced a new
challenging synthetic dataset with motion field ground truth, and also provide
extra ground truth for the object-instance matchings and corresponding
segmentation masks. We demonstrate that MaskFlow outperforms state of the art
methods when evaluated on our new challenging dataset, whilst still producing
comparable results on the popular FlyingThings3D benchmark dataset.

我们介绍了一种新颖的运动估计方法 MaskFlow，能够在小目标、大位移和外观剧变等极具挑战的情况下估计准确的运动场，通过利用物体级特征和分割，MaskFlow 能够近似物体的平移运动场，并提出了一种将不完整的平移运动场结合到后续的运动估计网络中进行精化和补全的有效方法。我们还提供了一个具有运动场 Ground Truth 的新的挑战性合成数据集，并为对象实例匹配和相应分割掩码提供了额外的 Ground Truth。我们证明，当在我们提供的新的挑战性数据集上评估时，MaskFlow 优于现有方法，同时在广受欢迎的 FlyingThings3D 基准数据集上也能产生可比较的结果。

MaskFlow: 对象感知的运动估计

MaskFlow: Object-Aware Motion Estimation

We propose a new long-term tracking performance evaluation methodology and
present a new challenging dataset of carefully selected sequences with many
target disappearances. We perform an extensive evaluation of six long-term and
nine short-term state-of-the-art trackers, using new performance measures,
suitable for evaluating long-term tracking - tracking precision, recall and
F-score. The evaluation shows that a good model update strategy and the
capability of image-wide re-detection are critical for long-term tracking
performance. We integrated the methodology in the VOT toolkit to automate
experimental analysis and benchmarking and to facilitate the development of
long-term trackers.

我们提出了一种新的长期跟踪性能评估方法，并提出了一个具有许多目标消失的精心选择序列的新挑战性数据集，通过使用适用于评估长期跟踪的新性能指标 - 跟踪精度，召回率和 F 得分来广泛评估了六个长期和九个短期的最先进的跟踪器，评估结果表明，良好的模型更新策略和全局检测能力对于长期跟踪性能至关重要，我们将该方法集成到 VOT 工具包中以自动化实验分析和基准测试，并促进了长期跟踪器的开发。