Skip to content

Advertisement

Open Access

A Conditional Entropy-Based Independent Component Analysis for Applications in Human Detection and Tracking

EURASIP Journal on Advances in Signal Processing20102010:468329

https://doi.org/10.1155/2010/468329

Received: 1 December 2009

Accepted: 12 April 2010

Published: 24 May 2010

Abstract

We present in this paper a modified independent component analysis (mICA) based on the conditional entropy to discriminate unsorted independent components. We make use of the conditional entropy to select an appropriate subset of the ICA features with superior capability in classification and apply support vector machine (SVM) to recognizing patterns of human and nonhuman. Moreover, we use the models of background images based on Gaussian mixture model (GMM) to handle images with complicated backgrounds. Also, the color-based shadow elimination and head models in ellipse shapes are combined to improve the performance of moving objects extraction and recognition in our system. Our proposed tracking mechanism monitors the movement of humans, animals, or vehicles within a surveillance area and keeps tracking the moving pedestrians by using the color information in HSV domain. Our tracking mechanism uses the Kalman filter to predict locations of moving objects for the conditions in lack of color information of detected objects. Finally, our experimental results show that our proposed approach can perform well for real-time applications in both indoor and outdoor environments.

Keywords

Support Vector MachineKalman FilterGaussian Mixture ModelColor InformationIndependent Component Analysis

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan
(2)
Department of Computer and Communication Engineering, China University of Technology, Hsinchu, Taiwan

Copyright

© Chin-Teng Lin et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement