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• W2804089744 abstract "Human action recognition is a topic widely studied over time, using numerous techniques and methods to solvea fundamental problem in automatic video analysis. Basically, a traditional human action recognition systemcollects video frames of human activities, extracts the desired features of each human skeleton and classify themto distinguish human gesture. However, almost all of these approaches roll out the space-time information of therecognition process. In this paper we present a novel use of an existing state-of-the-art space-time technique, theSpace-Time Interest Point (STIP) detector and its velocity adaptation, to human action recognition process. UsingSTIPs as descriptors and a Support Vector Machine classifier, we evaluate four different public video datasets tovalidate our methodology and demonstrate its accuracy in real scenarios." @default.
• W2804089744 created "2018-06-01" @default.
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• W2804089744 date "2017-01-01" @default.
• W2804089744 modified "2023-09-27" @default.
• W2804089744 title "Human action recognition in videos: a comparative evaluation of the classical and velocity adaptation space-time interest points techniques" @default.
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