Surveillance Video Processing Using Compressive Sensing
This addresses video processing for surveillance applications, but it appears incremental as it builds on existing compressive sensing and decomposition techniques.
The paper tackled the problem of segmenting background and extracting moving objects in surveillance videos by proposing a compressive sensing method combined with low-rank and sparse matrix decomposition, achieving reliable object extraction with a small amount of measurements.
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.