Human detection from hemispherical image based on probabilistic appearance model

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.