Human action recognition using double discriminative sparsity preserving projections and discriminant ridge-based classifier based on the GDWL-l1 graph

摘要

Human action recognition is defined as determining the actions of humans happening in video sequences. Human action recognition is one of the interesting topics which can play important role in intelligence, surveillance and health protection systems' performance; the performance of human action recognition methods is a vital issue that has been focused on many recent papers. Subspace learning and classification steps can impress the performance of the human action recognition methods. Accordingly, in this paper, new subspace learning and classification algorithms are proposed for human action recognition. Notably, graphs play important role to describe the relationship of data but most of the graph based methods used Euclidean distance metric. To overcome it, a geodesic distance based weighted LASSO l1-graph (GDWL-l1 graph) is proposed to extract the between-class and within-class graphs. Then, double discriminative sparsity preserving projections (DDSPP) algorithm is introduced to map the high-dimensional data to a new discriminant low-dimensional space using these graphs in order to have better discrimination besides sparsity and locality preserving in the mapped space. Subspace learning using DDSPP algorithm leads to a discriminative and sparse low-dimensional space. At the end, a discriminant ridge-based classifier (DRC) is introduced to inherit the grouping effect of the ridge regression besides incorporating the geodesic distance by defining a criterion of the classification. Experimental results show the suitable performance of the proposed method on HMDB51 and UCF101 datasets which are as 66.41% and 92.46%, respectively. (C) 2019 Elsevier Ltd. All rights reserved.