  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 an 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
