A method for human action recognition.pdf
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A method for human action recognition
Osama Masoud, Nikos Papanikolopoulos*
Department of Computer Science and Engineering, University of Minnesota, 4-192 EE/CS Building, 200 Union Street SE, Minneapolis, MN 55455, USA
Received 25 February 2002; received in revised form 4 March 2003; accepted 18 March 2003
Abstract
This article deals with the problem of classification of human activities from video. Our approach uses motion features that are computed
very efficiently, and subsequently projected into a lower dimensional space where matching is performed. Each action is represented as a
manifold in this lower dimensional space and matching is done by comparing these manifolds. To demonstrate the effectiveness of this
approach, it was used on a large data set of similar actions, each performed by many different actors. Classification results were very accurate
and show that this approach is robust to challenges such as variations in performers’ physical attributes, color of clothing, and style of motion.
An important result of this article is that the recovery of the three-dimensional properties of a moving person, or even the two-dimensional
tracking of the person’s limbs need not precede action recognition.
q 2003 Elsevier Science B.V. All rights reserved.
Keywords: Motion recognition; Human tracking; Articulated motion
1. Introduction
Recognition of human actions from video streams has
many applications in the surveillance, entertainment, user
interfaces, sports and video annotation domains. Given a
number of pre-defined actions, the problem can be stated as
that of classifying a new action into one of these actions.
Normally, the set of actions has a meaning in a certain
domain. In sign language, for example, the set of actions
corresponds to the set of possible words and letters that can
be produced. In ballet, the actions are the step names in one
of the ballet notation languages.
In psychophysics, the study of human body motion
perception by the human visual sys
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