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Received: 2008-06-25

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.7 P.976-984

http://doi.org/10.1631/jzus.A0820489


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


Author(s):  Yi ZHANG, Jie YANG, Kun LIU

Affiliation(s):  Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China

Corresponding email(s):   zypk@sohu.com

Key Words:  Moving objects recognition, Adaptive Gaussian mixture model, Principal component analysis, Linear discriminant analysis, Marginal Fisher analysis


Yi ZHANG, Jie YANG, Kun LIU. General moving objects recognition method based on graph embedding dimension reduction algorithm[J]. Journal of Zhejiang University Science A, 2009, 10(7): 976-984.

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Abstract: 
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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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