DocumentCode :
1148409
Title :
Learning a Scene Background Model via Classification
Author :
Lin, Horng-Horng ; Liu, Tyng-Luh ; Chuang, Jen-Hui
Author_Institution :
Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu
Volume :
57
Issue :
5
fYear :
2009
fDate :
5/1/2009 12:00:00 AM
Firstpage :
1641
Lastpage :
1654
Abstract :
Learning to efficiently construct a scene background model is crucial for tracking techniques relying on background subtraction. Our proposed method is motivated by criteria leading to what a general and reasonable background model should be, and realized by a practical classification technique. Specifically, we consider a two-level approximation scheme that elegantly combines the bottom-up and top-down information for deriving a background model in real time. The key idea of our approach is simple but effective: If a classifier can be used to determine which image blocks are part of the background, its outcomes can help to carry out appropriate blockwise updates in learning such a model. The quality of the solution is further improved by global validations of the local updates to maintain the interblock consistency. And a complete background model can then be obtained based on a measurement of model completion. To demonstrate the effectiveness of our method, various experimental results and comparisons are included.
Keywords :
image classification; support vector machines; background subtraction; image classification; interblock consistency; scene background model; support vector machines; two-level approximation; Background modeling; SVM; boosting; classification; tracking;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2009.2014810
Filename :
4776465
Link To Document :
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