Multi-object tracking in video using Localized Generalization Error model based RBFNN

Author

Ng, Wing W Y ; Ma, Xian-heng ; Chan, Patrick P K ; Yeung, Daniel S.

Author_Institution

Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China

Volume

4

fYear

2011

fDate

10-13 July 2011

Firstpage

1825

Lastpage

1831

Abstract

Objects in video are high level features which provide plenteous information for video analysis like video indexing, retrieval and understanding. In this paper, a new method of multi-object tracking in video has been proposed by combining Gaussian background modeling, Background subtraction, mean-shift algorithm and used Radial Basis Function Neural Network (RBFNN) optimized by the Localized Generalization Error to recognize and classify object candidates. Experimental results demonstrate that the proposed method yields a high accuracy in object tracking and less false tracking.

Keywords

Gaussian processes; object tracking; radial basis function networks; video signal processing; Gaussian background modeling; RBFNN; localized generalization error model; mean shift algorithm; radial basis function neural network; video analysis; video indexing; video multiobject tracking; video retrieval; Algorithm design and analysis; Classification algorithms; Computer vision; Feature extraction; Neurons; Tracking; Training; Localized generalization error model (L-GEM); Motion Segmentation; Multi-Object Tracking in Video; Radial basis Function Neural Network (RBFNN);

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6017033

Filename

6017033