Learning universal multiview dictionary for human action recognition

19 March 2020

Recently, many sparse coding based approaches have been proposed for human action recognition. However, most of them focus on learning a discriminative dictionary without explicitly taking into account the common patterns shared among different action classes. In this paper, we propose a novel discriminative dictionary learning framework by formulating a universal dictionary which consists of a shared sub-dictionary and a set of class-specific sub-dictionaries. As a result, inter-class differences can be better characterized with sparse codes obtained from the class-specific dictionaries. In addition, group sparsity and locality constraints are utilized to preserve the relationship and structure among features. In order to leverage the benefits of multiple descriptors, a dictionary is learned for each view, and the corresponding sparse representations of those descriptors are fused in a low dimensional feature space together with temporal information. The experimental results on three challenging datasets demonstrate that our method is able to achieve better performance than a number of stateof- the-art ones.