A Spatial-Extended Background Model for Moving Blobs Extraction in Indoor Environments
This paper presents a system for extracting regions of moving objects from an image sequence. To segment the foreground regions of ego-motion objects, we create a background model and update it using recent background variations. Since background images are usually changed in blobs, spatial relations are used to represent background appearances, which may be affected drastically by illumination changes and background object motion. To model the spatial relations, the joint colors of each pixel-pair are modeled as a mixture of Gaussian (MoG) distributions. Since modeling the colors of all pixel-pairs is expensive, the colors of pixel-pairs in a short distance are modeled. The pixel-pairs with higher mutual information are selected to represent the spatial relations in the background model. Experimental results show that the proposed method can efficiently detect the moving object regions when the background scene changes or the object moves around a region. By comparing with Gaussian background model and the MoG-based model, the proposed method can extract object regions more completely.
background modeling mixture of gaussian background subtraction object segmentation video surveillance