Abstract
Camera movement conveys spatial and narrative information essential for understanding video content. While recent camera movement classification (CMC) methods perform well on modern datasets, their generalization to historical footage remains unexplored. This paper presents the first systematic evaluation of deep video CMC models on archival film material. We summarize representative methods and datasets, highlighting differences in model design and label definitions. Five standard video classification models are assessed on the HISTORIAN dataset, which includes expert-annotated World War II footage. The best-performing model, Video Swin Transformer, achieves 80.25% accuracy, showing strong convergence despite limited training data. Our findings highlight the challenges and potential of adapting existing models to low-quality video and motivate future work combining diverse input modalities and temporal architectures.
Abstract (translated)
摄像机运动传达了理解视频内容所需的空间和叙事信息。虽然最近的摄像机运动分类（CMC）方法在现代数据集上表现良好，但它们对历史影像资料的泛化能力尚未被探索。本文首次系统地评估了深度视频 CMC 模型在档案电影材料上的性能。我们总结了代表性方法和数据集，并强调了模型设计和标签定义之间的差异。研究团队使用包含专家标注的世界大战 II 影像片段的 HISTORIAN 数据集，对五个标准视频分类模型进行了评估。表现最佳的模型——Video Swin Transformer，在仅有有限训练数据的情况下达到了 80.25% 的准确率，显示出强大的适应能力。 我们的研究成果突出了将现有模型应用于低质量视频所面临的挑战，并且为未来结合多种输入模式和时间架构的研究工作提供了动机。
URL
https://arxiv.org/abs/2510.14713
