<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Human action recognition using descriptor based on selective finite element analysis

Department of Electronics & Communication

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This paper proposes a novel local descriptor evaluated from the Finite Element Analysis for human action recognition. This local descriptor represents the...

Human action recognition using descriptor based on selective finite element analysis

Publié en ligne: 31 déc. 2019

Pages: 443 - 453

Reçu: 18 oct. 2019

DOI: https://doi.org/10.2478/jee-2019-0077

Mots clés
finite element analysis (FEA), stiffness matrix, discretization, support vector machine

© 2019 Rajiv Kapoor et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Human action recognition using descriptor based on selective finite element analysis

Publié en ligne: 31 déc. 2019

Pages: 443 - 453

Reçu: 18 oct. 2019

DOI: https://doi.org/10.2478/jee-2019-0077

Mots clésfinite element analysis (FEA), stiffness matrix, discretization, support vector machine

© 2019 Rajiv Kapoor et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
finite element analysis (FEA), stiffness matrix, discretization, support vector machine