Modeling the background and detecting moving objects based on Sift flow

Moving object detection in the presence of dynamic backgrounds remains a challenging problem in video surveillance. This paper presents a novel and efficient Sift flow-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive Sift flow descriptor that are calculated over a rectangle region around the pixel and the background model is dynamically updated. Experimental results on the publicly available video sequences demonstrate that the proposed approach provides an effective and efficient way for background modeling and motion detection.