We present DINO-Tracker -- a new framework for long-term dense tracking in
video. The pillar of our approach is combining test-time training on a single
video, with the powerful localized semantic features learned by a pre-trained
DINO-ViT model. Specifically, our framework simultaneously adopts DINO's
features to fit to the motion observations of the test video, while training a
tracker that directly leverages the refined features. The entire framework is
trained end-to-end using a combination of self-supervised losses, and
regularization that allows us to retain and benefit from DINO's semantic prior.
Extensive evaluation demonstrates that our method achieves state-of-the-art
results on known benchmarks. DINO-tracker significantly outperforms
self-supervised methods and is competitive with state-of-the-art supervised
trackers, while outperforming them in challenging cases of tracking under
long-term occlusions.

DINO-Tracker 是一种新的视频长期密集跟踪框架，通过在单个视频上进行测试时间训练，并结合预训练的 DINO-ViT 模型中学到的强大的局部语义特征，从而适应测试视频的动态观察，同时训练一种直接利用改进特征的跟踪器，通过自监督损失和正则化的组合实现端到端训练，并在已知基准测试中取得了最先进的结果；DINO-Tracker 在长期遮挡跟踪的挑战性案例中显著优于自监督方法，而且在竞争中超越了最先进的监督跟踪器。

DINO-Tracker：单视频中自监督点追踪的 DINO 驯化

DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single  Video

We introduce a novel semi-supervised video segmentation approach based on an
efficient video representation, called as "super-trajectory". Each
super-trajectory corresponds to a group of compact trajectories that exhibit
consistent motion patterns, similar appearance and close spatiotemporal
relationships. We generate trajectories using a probabilistic model, which
handles occlusions and drifts in a robust and natural way. To reliably group
trajectories, we adopt a modified version of the density peaks based clustering
algorithm that allows capturing rich spatiotemporal relations among
trajectories in the clustering process. The presented video representation is
discriminative enough to accurately propagate the initial annotations in the
first frame onto the remaining video frames. Extensive experimental analysis on
challenging benchmarks demonstrate our method is capable of distinguishing the
target objects from complex backgrounds and even reidentifying them after
long-term occlusions.

基于超轨迹的半监督视频分割方法，使用概率模型生成轨迹并采用密度峰值聚类算法进行可靠的轨迹分组，从而实现精确的目标物体提取和重识别。