DocumentCode :
456970
Title :
Robust Recursive Learning for Foreground Region Detection in Videos with Quasi-Stationary Backgrounds
Author :
Tavakkoli, Alireza ; Nicolescu, Mircea ; Bebis, George
Author_Institution :
Comput. Vision Lab., Nevada Univ., Reno, NV
Volume :
1
fYear :
0
fDate :
0-0 0
Firstpage :
315
Lastpage :
318
Abstract :
Detecting regions of interest in video sequences is the most important task in many high level video processing applications. In this paper a robust technique based on recursive learning of video background and foreground models is presented. Our contributions can be described along four directions. First, a recursive learning scheme is developed to build pixel models based on their colors. Second, we generate background and foreground models to enforce the temporal consistency of detected foregrounds. Third, we exploit dependencies between pixel colors to insure that the model is not restricted to using only independent features. Finally, an adaptive pixel-wise criterion is proposed that incorporates different spatial situations in the scene
Keywords :
image colour analysis; image sequences; learning (artificial intelligence); nonparametric statistics; video signal processing; foreground region detection; pixel colors; quasistationary backgrounds; recursive learning; temporal consistency; video foreground; video processing; video sequences; Application software; Cameras; Computer vision; Convergence; Kernel; Laboratories; Layout; Robustness; Surveillance; Video sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.1015
Filename :
1698896
Link To Document :
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