%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/08.12.16.50
%2 sid.inpe.br/sibgrapi/2016/08.12.16.50.05
%T Human Action Identification in Videos using Descriptor with Autonomous Fragments and Multilevel Prediction
%D 2016
%A Alcantara, Marlon Fernandes de,
%A Pedrini, Hélio,
%@affiliation Universidade Estadual de Campinas
%@affiliation Universidade Estadual de Campinas
%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 Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K action recognition, machine learning, computer vision.
%X Recent technological advances have provided devices with high processing power and storage capacities. Video cameras are found in several places, such as banks, airports, schools, supermarkets, streets, homes and industries. However, most of the video analysis tasks are still performed by human operators influenced by factors such stress and fatigue. This work proposes and evaluates a methodology for identifying common human actions by means of a CMSIP descriptor applied to a multilevel prediction scheme with retraining. The approach is built by dividing the descriptor into portions considered and interpreted independently by following distinct ways on the classification model, such that, a central mechanism will be responsible for deciding which action is being observed. Our method has proved to be fast and with accuracy compatible to the state-of-the-art on known public data sets. Furthermore, the developed prototype demonstrated to be a promising tool for real-time applications.
%@language en
%3 paper.pdf