Recognition of inter-class variation of human actions in sports video

Abstract

Human action recognition encompasses a scope for an automatic analysis of current events from video and has varied applications in multi-various fields. Recognizing and understanding of human actions from videos still remains a difficult downside as a result of the massive variations in human look, posture and body size inside identical category. This paper focuses on a specific issue related to inter-class variation in Human Action Recognition. To discriminate the human actions among the category, a novel approach which is based on wavelet packet transformation for feature extraction. As we are concentrating on classifying similar actions non-linearity among the features are analyzed and discriminated by proposed by Deterministic Normalized –Linear Discriminant Analysis (DN-LDA). However the major part of the recognition system relays on classification part and the dynamic feeds are classified by Hidden Markov Model at the final stage based on rule set. With a trained dataset and rules framed with the end user, our intelligent HAR system is capable of achieving the accuracy rate of 97.4% which is higher than the other state of art approaches. Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the inter-class variation.