Open AccessResearch on Human Action Recognition Algorithm Based on Sine Feature
Author(s) -
Haiyun Zhang,
Huazhou Dou,
Bo Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012024
Subject(s) - feature (linguistics) , artificial intelligence , discriminative model , pattern recognition (psychology) , skeleton (computer programming) , human skeleton , computer science , support vector machine , feature vector , sine , action (physics) , point (geometry) , joint (building) , mathematics , engineering , philosophy , linguistics , physics , geometry , quantum mechanics , programming language , architectural engineering
Recent studies have shown that exploring features of the skeleton data is vital for human action recognition. Nevertheless, how to effectively extract discriminative features is still a challenging work. In this paper, we propose a novel method that extracts a sine feature for human action recognition from skeleton data. Kinect is used to extract human skeleton information (3D coordinates of each joint point) firstly, then two joint points are connected to define a skeleton vector according to the principle of human body structure, and the sine feature of each skeleton vector is calculated as a new pose description feature, SVM is used to classify the obtained features for human action classification. Experimental results on Cornell Activity Database are provided, and the results demonstrate the effectiveness of our approach.