Robust Human Detection with Low Energy Consumption in Visual Sensor Network

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.