Abstract. A compressive sensing method combined with decomposition of a matrix formed with image frames of a surveillance video into low rank and sparse matrices is proposed to segment the background and extract moving objects in a surveillance video. The video is acquired by compressive measurements, and the measurements are used to reconstruct the video by a low rank and sparse decomposition of matrix. The low rank component represents the background, and the sparse component is used to identify moving objects in the surveillance video. The decomposition is performed by an augmented Lagrangian alternating direction method. Experiments are carried out to demonstrate that moving objects can be reliably extracted with a small amount of measurements.
Jiang et al. REF developed a low rank and sparse decomposition based approach to detect moving objects from a video.
Surveillance Video Processing Using Compressive Sensing
Inverse Problems and Imaging, Volume 6, No. 2, 2012, 201-214
Computer Science Mathematics