RT Conference Proceedings
T1 Inter and Intra Class Correlation Analysis (IIcCA) for Human Action Recognition in Realistic Scenarios
A1 Nazir, Saima
A1 Yousaf, Muhammad Haroon
A1 Velastin Carroza, Sergio Alejandro
AB Human action recognition in realistic scenarios is an important yet challenging task. In this paper we propose a new method, Inter and Intra class correlation analysis (IICCA), to handle inter and intra class variations observed in realistic scenarios. Our contribution includes learning a class specific visual representation that efficiently represents a particular action class and has a high discriminative power with respect to other action classes. We use statistical measures to extract visual words that are highly intra correlated and less inter correlated. We evaluated and compared our approach with state-of-the-art work using a realistic benchmark human action recognition dataset.
PB The Institution Of Engineering And Technology
SN 978-1-78561-652-5
YR 2017
FD 2017-07-11
LK https://hdl.handle.net/10016/28931
UL https://hdl.handle.net/10016/28931
LA eng
NO This paper has been presented at : 8th International Conference of Pattern Recognition Systems (ICPRS 2017)
NO S.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, the Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) the Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander.
DS e-Archivo
RD 20 may. 2024