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In this paper, we present a frame rate upconversion method using optical flow motion estimation and a patch-based reconstruction scheme. First, forward and backward motion vectors (MVs) are obtained using an optical flow algorithm, and reconstructed versions of the current and previous frames are generated by our patch-based reconstruction scheme. Using the original and reconstructed versions of the current and previous frames, two mismatch masks are obtained. Then, two versions of the middle frame are generated using a patch-based scheme, with estimated MVs and the current and previous frames. Finally, a middle mask, which identifies the mismatch areas of the two middle frames, is reconstructed. Using these three masks, the best candidates for interpolation are selected and fused to obtain the final middle frame. Due to the patch-based nature of our reconstruction scheme, most of the holes and cracks will be filled. Although there is always a probability of having holes, the size and number of such holes are much smaller than those that would be generated using pixel-based mapping. The rare holes are filled using existing hole-filling algorithms. The experimental results and a comparison of our method with existing algorithms show that our method performs better in terms of both objective and subjective quality of the final interpolated frames. The average peak signal-to-noise ratio (PSNR) improvement of our method is 1–2 dB. [ABSTRACT FROM AUTHOR]

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 foreground-background 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 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. [ABSTRACT FROM AUTHOR]