Optimizing human action recognition based on a cooperative coevolutionary algorithm

Vision-based human action recognition is an essential part of human behavior analysis, which is currently in great demand due to its wide area of possible applications. In this paper, an optimization of a human action recognition method based on a cooperative coevolutionary algorithm is proposed. By means of coevolution, three different populations are evolved to obtain the best performing individuals with respect to instance, feature and parameter selection. The fitness function is based on the result of the human action recognition method. Using a multi-view silhouette-based pose representation and a weighted feature fusion scheme, an efficient feature is obtained, which takes into account the multiple views and their relevance. Classification is performed by means of a bag of key poses, which represents the most characteristic pose representations, and matching of sequences of key poses. The performed experimentation indicates that not only a considerable performance gain is obtained outperforming the success rates of other state-of-the-art methods, but also the temporal and spatial performance of the algorithm is improved.