Feature Selection Based on AdaBoost in Video Surveillance System

Author

Tian, Bin ; Zheng, Xiaoshi ; Zhang, Rangyong ; Zhao, Yanling

Author_Institution

Shandong Inst. Of Light Ind., Jinan, China

Volume

4

fYear

2009

fDate

10-11 Oct. 2009

Firstpage

70

Lastpage

72

Abstract

At present, feature-based classification method is widely used in video surveillance system. How to find a group of features which are stable and efficient is concerned by researchers. In this paper, a new method based on AdaBoost is proposed to form a good sub-set of features. This method evaluates the performance of each feature, and then selects features from the extracted features for classification. Under the premise of ensuring the classification accuracy, the speed of the classifier is greatly improved.

Keywords

feature extraction; image classification; learning (artificial intelligence); video surveillance; AdaBoost; feature extraction; feature selection; feature-based classification method; video surveillance system; Application software; Automation; Computer science; Dispersion; Equations; Feature extraction; Humans; Shape measurement; Vehicles; Video surveillance; AdaBoost; feature selection; object classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on

Conference_Location

Changsha, Hunan

Print_ISBN

978-0-7695-3804-4

Type

conf

DOI

10.1109/ICICTA.2009.733

Filename

5288213