This paper proposes an adaptive color-guided autoregressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We observe and verify that the AR model tightly fits depth maps of generic scenes. The depth recovery task is formulated into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. We analyze the stability of our method from a linear system point of view, and design a parameter adaptation scheme to achieve stable and accurate depth recovery. Quantitative and qualitative evaluation compared with ten state-of-the-art schemes show the effectiveness and superiority of our method. Being able to handle various types of depth degradations, the proposed method is versatile for mainstream depth sensors, time-of-flight camera, and Kinect, as demonstrated by experiments on real systems.

Separation of video clips into foreground and background components is a useful and important technique, making recognition, classification and scene analysis more efficient. In this paper, we propose a motion-assisted matrix restoration (MAMR) model for foreground-background separation in video clips. In the proposed MAMR model, the backgrounds across frames are modeled by a low-rank matrix, while the foreground objects are modeled by a sparse matrix. To facilitate efficient foregroundbackground separation, a dense motion field is estimated for each frame, and mapped into a weighting matrix which indicates the likelihood that each pixel belongs to the background. Anchor frames are selected in the dense motion estimation to overcome the difficulty of detecting slowly-moving objects and camouflages. In addition, we extend our model to a robust MAMR model (R-MAMR) against noise for practical applications. Evaluations on challenging datasets demonstrate that our method outperforms many other state-of-the-art methods, and is versatile for a wide range of surveillance videos.

No abstract

Background extraction from video sequences is a useful and important technique in video surveillance. This paper proposes a motion-assisted matrix completion model for background extraction from video sequences. A binary motion map is first calculated for each frame by optical flow. By excluding areas associated with moving objects with the binary motion maps, the background extraction is formulated into a motion-assisted matrix completion (MAMC) problem. Experimental results show that our method not only extracts promising backgrounds but also outperforms many state-ofthe-art methods in distinguishing moving objects on challenging datasets.

No abstract