Human action recognition using an improved string edit distance
In this paper we propose an improvement of a human action recognition method that uses a string-based representation and a string edit distance to compare the observed action with reference actions in the training set. In particular, the original improvement is based on a specific formulation of the string edit distance that is more suited to take into account the problems related to noise and to different execution speeds that are observed in an action recognition system. The experimentation has been carried out on two widely adopted datasets, namely the MIVIA and the MHAD datasets, and the obtained results, compared with both the original method and other state of the art approaches, confirm the significance of the proposed improvement and the effectiveness of the method.