NEIGHBORING IMAGE PATCHES EMBEDDING FOR BACKGROUND MODELING

We present a novel feature extraction framework, Neighboring Image Patches Embedding (NIPE), for robust and efficient background modeling. We divide image into patches and represent each image patch as a NIPE vector. Then, the background model of each image patch is constructed as a group of weighted adaptive NIPE vectors. The NIPE feature vector, whose components are similarities between current image patch and its neighbors, describes mainly the mutual relationship between neighboring patches. Since neighboring image patches tend to be similarly affected by environmental effects (e.g., dynamic background), the NIPE vectors are more robust in these conditions comparing with the conventional method. Experimental results demonstrate the efficiency and effectiveness of our proposed NIPE method.