Robust Human Detection with Low Energy Consumption in Visual Sensor Network
Huiyuan Fu, Huadóng Ma, Liang Liu · 2011
In this paper, we try to address the difficult problem of detecting humans robustly with low energy consumption in the visual sensor network. The proposed method contains two parts: one is an ESOBS (Enhanced Self-Organizing Background Subtraction) based foreground segmentation module to obtain active areas in the observed area from the visual sensor; the other is a HOG (Histograms of Oriented Gradients) based detection module to detect the appearance shape from the foreground areas. Moreover, we create a large pedestrian dataset according to the specific scene in visual sensor networks. Numerous experiments are conducted. The experimental results show the effectiveness of our method.