Open AccessHuman action recognition using descriptor based on selective finite element analysis
Author(s) -
Rajiv Kapoor,
Om Mishra,
Madan Mohan Tripathi
Publication year - 2019
Publication title -
journal of electrical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.191
H-Index - 27
eISSN - 1339-309X
pISSN - 1335-3632
DOI - 10.2478/jee-2019-0077
Subject(s) - silhouette , artificial intelligence , pattern recognition (psychology) , computer science , action (physics) , support vector machine , radial basis function , feature (linguistics) , finite element method , stiffness matrix , action recognition , mathematics , discretization , classifier (uml) , matrix (chemical analysis) , computer vision , mathematical analysis , engineering , structural engineering , linguistics , philosophy , physics , materials science , quantum mechanics , artificial neural network , composite material , class (philosophy)
This paper proposes a novel local descriptor evaluated from the Finite Element Analysis for human action recognition. This local descriptor represents the distinctive human poses in the form of the stiffness matrix. This stiffness matrix gives the information of motion as well as shape change of the human body while performing an action. Initially, the human body is represented in the silhouette form. Most prominent points of the silhouette are then selected. This silhouette is discretized into several finite small triangle faces (elements) where the prominent points of the boundaries are the vertices of the triangles. The stiffness matrix of each triangle is then calculated. The feature vector representing the action video frame is constructed by combining all stiffness matrices of all possible triangles. These feature vectors are given to the Radial Basis Function-Support Vector Machine (RBF-SVM) classifier. The proposed method shows its superiority over other existing state-of-the-art methods on the challenging datasets Weizmann, KTH, Ballet, and IXMAS.