Action Recognition Using Effective Mask Patterns Selected from a Classificational Viewpoint

Abstract

This paper presents action recognition using effective mask patterns selected from an classificational viewpoint. Cubic higher-order local auto-correlation (CHLAC) feature is robust to position changes of human actions in a video, and its effectiveness for action recognition was already shown. However, the mask patterns for extracting cubic higher-order local auto-correlation (CHLAC) features are fixed. In other words, the mask patterns are independent of action classes, and the features extracted from those mask patterns are not specialized for each action. Thus, we propose automatic creation of specialized mask patterns for each action. Our approach consists of 2 steps. First, mask patterns are created by clustering of local spatio-temporal regions in each action. However, unnecessary mask patterns such as same patterns and mask patterns with all 0 or 1 are included. Then we select the effective mask patterns for classification by feature selection techniques. Through experiments using the KTH dataset, the effectiveness of our method is shown.