Moving object detection based on background dictionary

Gaussian Mixture Model (GMM) and its variations process images by per pixel, so they may be corrupted by noises and the computational cost is high. In this paper, we propose a robust moving object detection algorithm with a background dictionary learning. To do this, we first divide an image into multiple image patches that have the same sizes. Each patch is the object or background. Then, A background dictionary is learnt for each patch. The similarity between a patch and the background dictionary is measured, upon which a patch is distinguished between the object and the background. Additionally, in order to adapt the dynamic contexts across in a video sequence, a robust background dictionary updating scheme is proposed. Experimental results demonstrate the effectiveness and robustness of the proposed detection algorithm.