NDLI: A connectionist-based approach for human action identification

Content Provider	IEEE Xplore Digital Library
Author	Alazrai, R. Lee, C.S.G.
Copyright Year	2012
Description	Author affiliation: School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, U.S.A. (Alazrai, R.; Lee, C.S.G.)
Abstract	This paper presents a hierarchal, two-layer, connectionist-based human-action recognition system (CHARS) as a first step towards developing socially intelligent robots. The first layer is a K-nearest neighbor (K-NN) classifier that categorizes human actions into two classes based on the existence of locomotion, and the second layer consists of two multi-layer recurrent neural networks that distinguish between subclasses within each class. A pyramid of histograms of oriented gradients (PHOG) descriptor is proposed for extracting local and spatial features. The PHOG descriptor reduces the dimensionality of input space drastically, which results in better convergence for the learning and classification processes. Computer simulations were conducted to illustrate the performance of the proposed CHARS and the role of temporal factor in solving this problem. A widely used KTH human-action database and the human-action dataset from our lab were utilized for performance evaluation. The proposed CHARS was found to perform better than other existing human-action recognition methods and achieved a 95.55% recognition rate.
Starting Page	1212
Ending Page	1217
File Size	1454151
Page Count	6
File Format	PDF
ISBN	9781467314039
ISSN	10504729
e-ISBN	9781467314053
e-ISBN	9781467314046
DOI	10.1109/ICRA.2012.6224702
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Feature extraction Testing Training Recurrent neural networks Vectors Hidden Markov models Socially Intelligent Robots Human Actions Identification Recurrent Neural Networks Human-Robot Interaction
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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