Human action recognition using fusion of features for unconstrained video sequences

Abstract

Effective modeling of the human action using different features is a critical task for�human action recognition; hence, the fusion of features concept has been used in our proposed work. By fusing several modalities, features, or classifier decision scores, we present six different fusion models inspired by the early fusion schemes, late fusion schemes, and intermediate fusion schemes. In the first two models, we have utilized early fusion technique. The third and fourth models exploit intermediate fusion techniques. In the fourth model, we confront a kernel-based fusion scheme, which takes advantage of kernel basis of classifiers i.e.�Support Vector Machine�(SVM). In the fifth and sixth models, we have demonstrated late fusion techniques. The performance of all models is evaluated with ASLAN and UCF11 benchmark dataset of action videos. We obtained significant improvements with the proposed fusion schemes relative to the usual fusion schemes relative state-of-the-art methods.