CFP last date
20 June 2024
Reseach Article

Real Time Human Activity Recognition System based on Radon Transform

Published on None 2011 by Z.A. Khan, W. Sohn
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 4
None 2011
Authors: Z.A. Khan, W. Sohn

Z.A. Khan, W. Sohn . Real Time Human Activity Recognition System based on Radon Transform. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 4 (None 2011), 7-13.

author = { Z.A. Khan, W. Sohn },
title = { Real Time Human Activity Recognition System based on Radon Transform },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 4 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 7-13 },
numpages = 7,
url = { /specialissues/ait/number4/2843-224/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A Z.A. Khan
%A W. Sohn
%T Real Time Human Activity Recognition System based on Radon Transform
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%N 4
%P 7-13
%D 2011
%I International Journal of Computer Applications

A real time human activity recognition system based on Radon transform (RT), Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is presented. RT improves low frequency components and PCA provide global representation of these low frequency components in few eigenvectors. The proposed technique computes radon projections in different directions to obtain directional features of the images from video sequences. PCA is used to reduce the dimensions of radon shape features. LDA is applied on PCA features to provide better class separation. The aim is to develop a proficient recognition system in real time by the combination of local and global features. The dataset consisting of normal and abnormal activities is produced. Artificial Neural Nets (ANN) is used to recognize different human activities in real time. Experimental results show better recognition results for our system as compared to some state of the art methods.

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Index Terms

Computer Science
Information Sciences


Feature Extraction Radon Transform PCA LDA ANN PCA LDA ANN