The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences

TL;DR

The paper introduces GORA, a new signal alignment algorithm that outperforms DTW and FastDTW in speed and accuracy, offering a promising alternative for action recognition in video sequences.

Contribution

GORA provides a mathematically grounded, efficient, and accurate framework for signal alignment, improving over existing DTW-based methods.

Findings

GORA significantly outperforms DTW and FastDTW in computational efficiency.

GORA achieves higher accuracy in signal matching tasks.

Numerical verification confirms GORA's theoretical advantages.

Abstract

Signal alignment has become a popular problem in robotics due in part to its fundamental role in action recognition. Currently, the most successful algorithms for signal alignment are Dynamic Time Warping (DTW) and its variant 'Fast' Dynamic Time Warping (FastDTW). Here we introduce a new framework for signal alignment, namely the Globally Optimal Reparameterization Algorithm (GORA). We review the algorithm's mathematical foundation and provide a numerical verification of its theoretical basis. We compare the performance of GORA with that of the DTW and FastDTW algorithms, in terms of computational efficiency and accuracy in matching signals. Our results show a significant improvement in both speed and accuracy over the DTW and FastDTW algorithms and suggest that GORA has the potential to provide a highly effective framework for signal alignment and action recognition.