DocumentCode :
2348642
Title :
Human detection from hemispherical image based on probabilistic appearance model
Author :
Saito, Mamoru ; Kitaguchi, Katsuhisa
Author_Institution :
Osaka Municipal Tech. Res. Inst., Osaka, Japan
fYear :
2010
fDate :
3-5 March 2010
Firstpage :
1
Lastpage :
5
Abstract :
This paper presents a method for automated human detection using fisheye lens camera. We introduce a probabilistic model to describe the wide variation of human appearance in hemispherical image. In our method, a human is modeled as probabilistic shape features of body silhouette and head-shoulder contour. These features are extracted from the human images taken at various distance and orientation with respect to the camera, and form the training data set for template modeling. A Non linear template model is build by the combination of Principal Component Analysis (PCA) and Kernel Ridge Regression (KRR). Finally, the problem of human detection is formulated as maximum a posteriori (MAP) estimation using above model. Experiments are conducted on indoor space where a fisheye lens camera is installed on the ceiling of crossing hallway. The feasibility and accuracy of our method is discussed through the experimental results.
Keywords :
feature extraction; maximum likelihood estimation; principal component analysis; probability; automated human detection; body silhouette; fisheye lens camera; head-shoulder contour; hemispherical image; human appearance; kernel ridge regression; maximum a posteriori estimation; nonlinear template model; principal component analysis; probabilistic appearance model; probabilistic model; probabilistic shape features; Biological system modeling; Cameras; Data mining; Feature extraction; Humans; Kernel; Lenses; Principal component analysis; Shape; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, Control and Signal Processing (ISCCSP), 2010 4th International Symposium on
Conference_Location :
Limassol
Print_ISBN :
978-1-4244-6285-8
Type :
conf
DOI :
10.1109/ISCCSP.2010.5463415
Filename :
5463415
Link To Document :
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