With advances in optical sensor technology, heterogeneous camera systems are increasingly used for high-resolution (HR) video acquisition and analysis. However, motion transfer across multiple cameras poses challenges. To address this, we propose a algorithm based on time series analysis that identifies motion seasonality and constructs an additive model to extract transferable patterns. Validated on real-world data, our algorithm demonstrates effectiveness and interpretability. Notably, it improves pose estimation in low-resolution videos by leveraging patterns derived from HR counterparts, enhancing practical utility. Code is available at: https://github.com/IndigoPurple/TSAMT

翻译：随着光学传感器技术的进步，异质摄像头系统被越来越多地用于高分辨率视频采集与分析。然而，跨多摄像头的运动迁移仍面临挑战。为此，我们提出一种基于时间序列分析的算法，该算法能够识别运动季节性，并构建加性模型以提取可迁移的模式。通过在真实数据上的验证，该算法展现出有效性及可解释性。值得注意的是，该算法通过利用从高分辨率视频中提取的模式来提升低分辨率视频中的姿态估计性能，增强了实用价值。代码开源地址：https://github.com/IndigoPurple/TSAMT