%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.22.20.50
%2 sid.inpe.br/sibgrapi/2016/07.22.20.50.36
%@doi 10.1109/SIBGRAPI.2016.035
%T Decreasing the Number of Features for Improving Human Action Classification
%D 2016
%A Souza, Kleber Jacques de,
%A Araujo, Arnaldo de Albuquerque,
%A Jr, Zenilton Kleber G. do Patrociinio,
%A Cousty, Jean,
%A Najman, Laurent,
%A Kenmochi, Yukiko,
%A Guimaraes, Silvio Jamil F.,
%@affiliation NPDI/DCC/UFMG - Federal University of Minas Gerais - Computer Science Department - Belo Horizonte, MG, Brazil
%@affiliation NPDI/DCC/UFMG - Federal University of Minas Gerais - Computer Science Department - Belo Horizonte, MG, Brazil
%@affiliation Audio-Visual Information Proc. Lab. (VIPLAB) - Computer Science Department -- ICEI -- PUC Minas
%@affiliation Universite Paris-Est, Laboratoire d'Informatique Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162 Noisy-le-Grand France
%@affiliation Universite Paris-Est, Laboratoire d'Informatique Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162 Noisy-le-Grand France
%@affiliation Universite Paris-Est, Laboratoire d'Informatique Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162 Noisy-le-Grand France
%@affiliation Audio-Visual Information Proc. Lab. (VIPLAB) - Computer Science Department -- ICEI -- PUC Minas
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K Spatio-temporal video segmentation, human action classification, BossaNova representation.
%X Action classification in videos has been a very active field of research over the past years. Human action classification is a research field with application to various areas such as video indexing, surveillance, human-computer interfaces, among others. In this paper, we propose a strategy based on decreasing the number of features in order to improve accuracy in the human action classification task. Thus, to classify human action, we firstly compute a video segmentation for simplifying the visual information, in the following, we use a mid-level representation for representing the feature vectors which are finally classified. Experimental results demonstrate that our approach has improved the quality of human action classification in comparison to the baseline while using 60% less features.
%@language en
%3 PID4373569.pdf