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To address this issue, the authors propose a robust human action detection and recognition framework that works effectively on videos taken in controlled or uncontrolled environments. Specifically, the authors integrate the optical flow field and Harris3D corner detector to generate a new spatial-temporal information representation for each video sequence, from which the general Gaussian mixture model (GMM) is learned. All the mean vectors of the Gaussian components in the generated GMM model are concatenated to create the GMM supervector for video action recognition. They build a boosting classifier based on a set of sparse representation classifiers and hamming distance classifiers to improve the accuracy of action recognition. The experimental results on two broadly used public data sets, KTH and UCF YouTube Action, show that the proposed framework outperforms the other state-of-the-art approaches on both action detection and recognition.<\/p>","DOI":"10.4018\/ijmdem.2015010101","type":"journal-article","created":{"date-parts":[[2015,2,27]],"date-time":"2015-02-27T07:32:28Z","timestamp":1425022348000},"page":"1-18","source":"Crossref","is-referenced-by-count":30,"title":["Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled Environments"],"prefix":"10.4018","volume":"6","author":[{"given":"Dianting","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei-Ling","family":"Shyu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiru","family":"Zhao","sequence":"additional","affiliation":[{"name":"China Earthquake Networks Center, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"Computing and Software Systems, University of Washington Bothell, Bothell, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijmdem.2015010101-0","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.881199"},{"key":"ijmdem.2015010101-1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2006.870086"},{"key":"ijmdem.2015010101-2","first-page":"425","article-title":"Gmm supervector for content based music similarity.","author":"C.Charbuillet","year":"2011","journal-title":"International Conference on Digital Audio Effects"},{"key":"ijmdem.2015010101-3","first-page":"531","volume":"Vol. 2","author":"K.-T.Chuang","year":"2013","journal-title":"Modeling and recognizing action contexts in persons using sparse representation. 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