NDLI: Detection and identification of human actions using Predictive Modular Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Petridis, V. Deb, B. Syrris, V.
Copyright Year	2009
Description	Author affiliation: Department of Computer Science, University of Reading, United Kingdom (Deb, B.) \|\| Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece (Petridis, V.; Syrris, V.)
Abstract	The aim of the present study is to validate a 2D kinematic model of human body in providing considerable features that they could be used for human actions classification. Human motion can be termed as a non-rigid, articulated motion, with body parts being piecewise rigid, held together by joints. The presented approach uses the fact that the human body has certain anthropometric proportion and uses the anatomical shape representation of the non-rigid and articulated human body contour. The body joints and the different body parts are detected with help of prior anatomical knowledge and extracted silhouette. The result of this kinematics based approach is a simple 2D human stick figure. Features are extracted from this 2D model and used to represent the human body. In the training phase, each training video is represented by a neural network, while in classification phase, the Predictive Modular Neural Network (PREMONN) [12] time series classification algorithm is applied to classify the human actions.
Starting Page	406
Ending Page	411
File Size	226514
Page Count	6
File Format	PDF
ISBN	9781424446841
DOI	10.1109/MED.2009.5164575
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-24
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution human action recognition Biological system modeling Shape measurement Humans human detection Classification algorithms 2D human body modeling Neural networks Kinematics anthropometrics Introduction (Heading 1) PREMONN classification algorithm Computer networks Joints Testing
Content Type	Text
Resource Type	Article

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