Moving Target Classification in Video Sequences Based on Features Combination and SVM

Author

Kong, Yinghui ; Wang, Lei

Author_Institution

Dept. of Electron. & Commun. Eng., North China Electr. Power Univ., Baoding, China

fYear

2010

fDate

10-12 Dec. 2010

Firstpage

1

Lastpage

4

Abstract

Moving target classification plays a very important role in intelligent video surveillance system. A method for moving target classification in video sequences based on features combination and SVM is presented in this paper. In this method, single Gaussian background model based on the background difference method is used to achieve the motion detection, Hu moment features in moving target are extracted, and then Support Vector Machine (SVM) is used to classify the moving target, human, animal (dog), vehicle and bike. To solve the problem of low classification ratio for human and animal, the other features, Area and euler number, are added, and classification ratio is improved.

Keywords

feature extraction; image classification; image motion analysis; image sequences; support vector machines; video surveillance; Gaussian background model; Hu moment features; SVM; background difference method; intelligent video surveillance system; motion detection; moving target classification; support vector machine; video sequences; Animals; Classification algorithms; Feature extraction; Humans; Support vector machines; Training; Video sequences;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5391-7

Electronic_ISBN

978-1-4244-5392-4

Type

conf

DOI

10.1109/CISE.2010.5676969

Filename

5676969