RT Conference Proceedings
T1 Silhouette-based human action recognition with a multi-class support vector machine
A1 González, Luis
A1 Velastin Carroza, Sergio Alejandro
A1 Acuña Leiva, Gonzalo
AB Computer vision systems have become increasingly popular, being used to solve a wide range of problems. In this paper, a computer vision algorithm with a support vector machine (SVM) classifier is presented. The work focuses on the recognition of human actions through computer vision, using a multi-camera dataset of human actions called MuHAVi. The algorithm uses a method to extract features, based on silhouettes. The challenge is that in MuHAVi these silhouettes are noisy and in many cases include shadows. As there are many actions that need to be recognised, we take a multiclass classification ap-proach that combines binary SVM classifiers. The results are compared with previous results on the same dataset and show a significant improvement, especially for recognising actions on a different view, obtaining overall accuracy of 85.5% and of 93.5% for leave-one-camera-out and leave-one-actor-out tests respectively.
PB Institution Of Engineering And Technology (IET)
SN 978-1-78561-887-1
YR 2018
FD 2018-05
LK https://hdl.handle.net/10016/28970
UL https://hdl.handle.net/10016/28970
LA eng
NO This paper has been presented at : 9th International Conference on Pattern Recognition Systems (ICPRS 2018)
NO Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander.
DS e-Archivo
RD 18 jul. 2024