The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences
This provides a more efficient and accurate framework for signal alignment, benefiting robotics and action recognition applications, though it appears incremental as an alternative to existing methods.
The paper tackles the problem of signal alignment for action recognition in video sequences by introducing the Globally Optimal Reparameterization Algorithm (GORA), which shows significant improvements in both speed and accuracy over Dynamic Time Warping and FastDTW algorithms.
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.