Cross-Camera Human Motion Transfer by Time Series Analysis
This work addresses motion transfer challenges in multi-camera systems, enhancing practical utility for video analysis applications, but it appears incremental as it builds on existing time series methods.
The paper tackles the problem of transferring human motion across heterogeneous camera systems by proposing a time series analysis algorithm that identifies motion seasonality and constructs an additive model to extract transferable patterns. It improves pose estimation in low-resolution videos by leveraging patterns from high-resolution counterparts, demonstrating effectiveness and interpretability on real-world data.
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