Self-Supervised Tracking via Target-Aware Data Synthesis

TL;DR

This paper introduces a self-supervised learning approach for visual tracking that synthesizes training data through target-aware data augmentation, reducing reliance on annotated datasets and enhancing tracking robustness.

Contribution

The authors propose the Crop-Transform-Paste data synthesis method, enabling effective self-supervised training of trackers without human annotations, adaptable to existing frameworks.

Findings

Achieves competitive performance with limited annotations

Improves robustness against occlusion, deformation, and background clutter

Enhances various state-of-the-art trackers with self-supervised training

Abstract

While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training. To eliminate expensive and exhaustive annotation, we study self-supervised learning for visual tracking. In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data by simulating various appearance variations during tracking, including appearance variations of objects and background interference. Since the target state is known in all synthesized data, existing deep trackers can be trained in routine ways using the synthesized data without human annotation. The proposed target-aware data-synthesis method adapts existing tracking approaches within a self-supervised learning framework without algorithmic changes. Thus, the proposed self-supervised learning mechanism can be seamlessly integrated into existing tracking frameworks to perform training. Extensive experiments show that our method 1) achieves favorable performance against supervised learning schemes under the cases with limited annotations; 2) helps deal with various tracking challenges such as object deformation, occlusion, or background clutter due to its manipulability; 3) performs favorably against state-of-the-art unsupervised tracking methods; 4) boosts the performance of various state-of-the-art supervised learning frameworks, including SiamRPN++, DiMP, and TransT.