Generalized Time Warping for Multi-modal Alignment of Human Motion

Abstract

Temporal alignment of human motion has been a topic of recent interest due to its applications in animation, tele-rehabilitation and activity recognition among others. This paper presents generalized time warping (GTW), an extension of dynamic time warping (DTW) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GTW solves three major drawbacks of existing approaches based on DTW: (1) GTW provides a feature weighting layer to adapt different modalities (e.g., video and motion capture data), (2) GTW extends DTW by allowing a more flexible time warping as combination of monotonic functions, (3) unlike DTW that typically incurs in quadratic cost, GTW has linear complexity. Experimental results demonstrate that GTW can efficiently solve the multi-modal temporal alignment problem and outperforms state-of-the-art DTW methods for temporal alignment of time series within the same modality.