Robust moving object detection using compressed sensing

Moving object detection plays a key role in video surveillance. A number of object detection methods have been proposed in the spatial domain. In this study, the authors propose a compressed sensing-based algorithm for the detection of moving object. They first use a practical three-dimensional circulant sampling method to yield sampled measurements. Then, they propose an object detection model to simultaneously reconstruct the foreground support, background and video sequence using the sampled measurements directly. Experimental results show that the proposed moving object detection algorithm outperforms the state-of-the-art approaches and it is robust to the movement turbulence, camera motion and video noise.