General moving objects recognition method based on graph embedding dimension reduction algorithm

被引：0

作者：

Zhang, Yi ^{[1
]}

Yang, Jie ^{[1
]}

Liu, Kun ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China

来源：

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A | 2009年 / 10卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Moving objects recognition; Adaptive Gaussian mixture model; Principal component analysis; Linear discriminant analysis; Marginal Fisher analysis; SURVEILLANCE; MOTION;

D O I：

10.1631/jzus.A0820489

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.

引用

页码：976 / 984

页数：9

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