DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video (2403.14548v2)

Abstract: 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.

• The paper introduces a self-supervised framework that leverages pre-trained DINO-ViT features combined with test-time training for refined point tracking.
• The method predicts a feature residual to enhance DINO's semantic output, effectively managing both short-term motion and long-term occlusions.
• The approach establishes a new benchmark in dense point tracking and paves the way for improved video analysis in challenging scenarios.

Leveraging DINO for Enhanced Self-Supervised Point Tracking in Videos

Introduction

Video analysis and understanding constitute a core part of contemporary AI and computer vision research. A fundamental task in this domain is tracking, which involves identifying the movement of objects or points across video frames. The novel paper "DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video" introduces a framework that adeptly combines the strengths of a pre-trained DINO-ViT model with test-time training tailored to single videos, achieving state-of-the-art results in self-supervised point tracking.

Method Overview

The proposed DINO-Tracker framework innovates by refining features from the pre-trained DINO model to cater to tracking specificities observed in a given video. This is achieved by predicting a residual to DINO’s features which, when combined with the original feature set, yields refined features adept at tracking across frames. The method is distinctive in leveraging test-time training, focusing on refining the model’s output based on the video at hand, and employing self-supervised losses alongside regularization to maintain the beneficial semantic prior knowledge embedded within DINO's features.

Key Contributions

1. Integration of Pre-trained DINO Features: This research pioneers the application of DINO's powerful semantic features to the task of dense point tracking, highlighting DINO's versatility beyond its initial use cases in image-based tasks.
2. Self-Supervised Framework with Test-Time Optimization: The methodology introduces an effective way to adapt pre-trained models to specific videos through test-time training, a novel approach in the field of video tracking.
3. Robust Tracking Through Occlusions: By refining DINO's features to better capture motion and employing a comprehensive loss function, the framework demonstrates a significant improvement in its ability to track points through long-term occlusions compared to existing methods.

Technical Insights

The loss functions formulated for training serve to capture both short-term motion patterns through pseudo ground truth optical flows and long-term semantic feature-level correspondences. These elements together facilitate accurate point tracking even in challenging scenarios involving rapid object movements or prolonged occlusions. Particularly, the paper showcases the complementary nature of appearance-based features and semantic information, crafting a synergy that substantially enhances tracking performance.

Practical and Theoretical Implications

DINO-Tracker’s achievement in utilizing pre-trained DINO features for video tracking opens up new avenues for the application of image-based models to video-related tasks. The test-time training approach incorporated into the framework further underscores the potential for fine-tuning general-purpose models on specific videos, promising tailor-made solutions for video tracking challenges. From a practical standpoint, the ability to accurately track points through occlusions with minimal supervision is particularly relevant for applications in surveillance, sports analytics, and interactive media, where tracking consistency and reliability are paramount.

Future Directions

The research provides a compelling foundation for future exploration in several directions. One potential area is the development of methods to predict the trajectories of occluded points, which could further improve tracking accuracy. Additionally, investigating the adaptability of other pre-trained image models for video tracking tasks, inspired by the success of integrating DINO's features, could expand the toolkit available for tackling diverse tracking scenarios.

Conclusions

"DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video" stands out as a noteworthy advancement in self-supervised video tracking. By ingeniously combining the semantic insights of DINO features with the flexibility of test-time optimization, the paper sets a new benchmark for tracking performance, particularly in handling occlusions. This work not only enriches the current understanding and capabilities in video tracking but also paves the way for further innovative applications of pre-trained image models in video analysis tasks.