Moving object detection in dynamic scenes using nonparametric local kernel histogram estimation

Robust detection of moving objects in complex and dynamic scenes is one of the most challenging issues in computer vision. In this paper, we present an approach to segmenting moving objects with nonparametric estimated local kernel histogram (ELKH) in dynamic scenes. By using the correlation and texture of spatially proximal pixels, local kernel histogram background model is constructed. Then probability distribution of local kernel histogram is estimated with nonparametric techniques. We employ Bhattacharyya distance to measure the similarity of local kernel histogram between estimated background model with each pixel in current frame, and decide whether the pixel belongs to moving objects or not. Our approach can reduce false detections due to disturbing noise and small motions in dynamic scenes, such as swaying branches, flickering water surface, rain. Experiments show that the proposed approach achieves promising results in real dynamic videos robustly.