无需标注数据，用传统方法实现高效在线视频稳像。
No Labels, No Look-Ahead: Unsupervised Online Video Stabilization with Classical Priors
- 采用三阶段经典流程+多线程缓冲，避免深度学习依赖
- 在新构建的无人机夜视数据集上显著优于现有在线方法
- 适合资源受限设备，适用于夜间无人机等特殊场景
本文提出一种新的无监督在线视频稳像框架。不同于依赖成对稳定与抖动视频数据集的深度学习方法，本方法基于经典稳像流程，设计三阶段处理结构并引入多线程缓冲机制，有效解决端到端学习中数据有限、控制性差及硬件资源受限下的效率问题。现有基准主要聚焦可见光前视手持视频，限制了稳像技术在无人机夜间遥感等领域的应用。为此，我们构建了首个多模态无人机航拍视频数据集（UAV-Test）。实验表明，该方法在定量指标与视觉质量上均持续优于当前最先进的在线稳像算法，性能接近离线方法。
原文摘要 · Abstract (English)
We propose a new unsupervised framework for online video stabilization. Unlike methods based on deep learning that require paired stable and unstable datasets, our approach instantiates the classical stabilization pipeline with three stages and incorporates a multithreaded buffering mechanism. This design addresses three longstanding challenges in end-to-end learning: limited data, poor controllability, and inefficiency on hardware with constrained resources. Existing benchmarks focus mainly on handheld videos with a forward view in visible light, which restricts the applicability of stabilization to domains such as UAV nighttime remote sensing. To fill this gap, we introduce a new multimodal UAV aerial video dataset (UAV-Test). Experiments show that our method consistently outperforms state-of-the-art online stabilizers in both quantitative metrics and visual quality, while achieving performance comparable to offline methods.
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