Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation
This is an incremental improvement for video processing applications, enhancing separation quality in online settings.
The paper tackles online video foreground-background separation by proposing a compressive online RPCA method with optical flow, which recursively processes frames into sparse and low-rank components using prior information and motion compensation, and it outperforms existing methods in visual and quantitative results.
In the context of online Robust Principle Component Analysis (RPCA) for the video foreground-background separation, we propose a compressive online RPCA with optical flow that separates recursively a sequence of frames into sparse (foreground) and low-rank (background) components. Our method considers a small set of measurements taken per data vector (frame), which is different from conventional batch RPCA, processing all the data directly. The proposed method also incorporates multiple prior information, namely previous foreground and background frames, to improve the separation and then updates the prior information for the next frame. Moreover, the foreground prior frames are improved by estimating motions between the previous foreground frames using optical flow and compensating the motions to achieve higher quality foreground prior. The proposed method is applied to online video foreground and background separation from compressive measurements. The visual and quantitative results show that our method outperforms the existing methods.