Cross-Camera Human Motion Transfer by Time Series Analysis

Abstract

Along with advances in optical sensors is the increasingly common practice of building an imaging system with heterogeneous cameras. While high-resolution (HR) video acquisition and analysis benefit from hybrid sensors, the intrinsic characteristics of multiple cameras lead to a challenging motion transfer problem. In this paper, we propose an algorithm using time series analysis for motion transfer among multiple cameras. Specifically, we first identify seasonality in the motion data, and then build an additive time series model to extract patterns that could be transferred across different cameras. Our approach has a complete and clear mathematical formulation, and the algorithm is also efficient and interpretable. Through the experiment on real-world data, we demonstrate the effectiveness of our method. Furthermore, our motion transfer algorithm could combine with and facilitate downstream tasks, e.g., enhancing pose estimation on low-resolution (LR) videos with inherent patterns extracted from HR ones.Comment: 10 pages, 9 figure