Recognition of Human Actions using an Optimal Control Based Motor Model
Ganesh, S.
Bajcsy, R.
California Univ., Berkeley, CA;
This paper appears in: Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop on
Publication Date: 7-9 Jan. 2008
On page(s): 1-6
Location: Copper Mountain, CO,
ISSN: 1550-5790
ISBN: 978-1-4244-1913-5
INSPEC Accession Number: 10060566
Digital Object Identifier: 10.1109/WACV.2008.4544021
Current Version Published: 2008-06-17
Abstract
We present a novel approach to the problem of representation and recognition of human actions, that uses an optimal control based model to connect the high-level goals of a human subject to the low-level movement trajectories captured by a computer vision system. These models quantify the high-level goals as a performance criterion or cost function which the human sensorimotor system optimizes by picking the control strategy that achieves the best possible performance. We show that the human body can be modeled as a hybrid linear system that can operate in one of several possible modes, where each mode corresponds to a particular high-level goal or cost function. The problem of action recognition, then is to infer the current mode of the system from observations of the movement trajectory. We demonstrate our approach on 3D visual data of human arm motion.
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