Human Action Recognition Using Temporal Sequence Alignment

In this paper, we address the problem of recognizing human actions from videos. Human actions recognition is a challenging task in computer vision. We propose a method to solve this problem using Longest Common Sub-Sequence (LCSS) algorithm and Shape Context (SC). Our contributions in this paper are twofold. First, we show the applicability of the SC as a pairwise shape-similarity measurement for generating a sequence that defines a specific motion. Secondly, we demonstrate the usability of LCSS to classify human actions. Experiments were performed on two action datasets to compare the result to the related methods.