caa a22 4500 605447985 CHVBK 20210128100134.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s11042-014-1910-9 doi (NATIONALLICENCE)springer-10.1007/s11042-014-1910-9 A method for action recognition based on pose and interest points [Elektronische Daten] [Lu Lu, Yi-Ju Zhan, Qing Jiang, Qing-ling Cai] In recent years, action recognition has become a hot research topic in the image processing area. Some studies have shown that based on supervised learning, spatial-temporal interest points which are extracted from videos demonstrate good performance in human action recognition. In this paper, we define the attributes of human pose, and associate human pose with interest points for human action recognition. We find that interest points can be used as samplers of the particle filter method, and improve the precision of pose estimation. Human pose can be used to detect outliers in interest points, and improve the precision of action recognition. Location and density of interest points associated with human pose can also improve the precision of action recognition. Experiment results on the publicly available "Weizmann”, "KTH” and "UIUC” dataset demonstrate that our method outperforms the state-of-the-art methods. Springer Science+Business Media New York, 2014 Action recognition nationallicence Interest points nationallicence Particle filter nationallicence Pose estimation nationallicence Lu Lu School of Engineering, Sun Yat-sen University, 510006, Guangzhou, China aut Zhan Yi-Ju School of Engineering, Sun Yat-sen University, 510006, Guangzhou, China aut Jiang Qing School of Engineering, Sun Yat-sen University, 510006, Guangzhou, China aut Cai Qing-ling School of Engineering, Sun Yat-sen University, 510006, Guangzhou, China aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 6091-6109 1380-7501 74:15<6091 2015 74 11042 https://doi.org/10.1007/s11042-014-1910-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-1910-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lu Lu School of Engineering, Sun Yat-sen University, 510006, Guangzhou, China aut NATIONALLICENCE 700 1- Zhan Yi-Ju School of Engineering, Sun Yat-sen University, 510006, Guangzhou, China aut NATIONALLICENCE 700 1- Jiang Qing School of Engineering, Sun Yat-sen University, 510006, Guangzhou, China aut NATIONALLICENCE 700 1- Cai Qing-ling School of Engineering, Sun Yat-sen University, 510006, Guangzhou, China aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 6091-6109 1380-7501 74:15<6091 2015 74 11042