Learning to Segment Moving Objects in Videos

We segment moving objects in videos by ranking spatio-temporal segment proposals according to "moving objectness": how likely they are to contain a moving object. In each video frame, we compute segment proposals using multiple figure-ground segmentations on per frame motion boundaries. We rank them with a Moving Objectness Detector trained on image and motion fields to detect moving objects and discard over/under segmentations or background parts of the scene. We extend the top ranked segments into spatio-temporal tubes using random walkers on motion affinities of dense point trajectories. Our final tube ranking consistently outperforms previous segmentation methods in the two largest video segmentation benchmarks currently available, for any number of proposals. Further, our per frame moving object proposals increase the detection rate up to 7\% over previous state-of-the-art static proposal methods

Multi-step flow fusion: towards accurate and dense correspondences in long video shots

International audienceThe aim of this work is to estimate dense displacement fields over long video shots. Put in sequence they are useful for representing point trajectories but also for propagating (pulling) information from a reference frame to the rest of the video. Highly elaborated optical flow estimation algorithms are at hand, and they were applied before for dense point tracking by simple accumulation, however with unavoidable position drift. On the other hand, direct long-term point matching is more robust to such deviations, but it is very sensitive to ambiguous correspondences. Why not combining the benefits of both approaches? Following this idea, we develop a multi-step flow fusion method that optimally generates dense long-term displacement fields by first merging several candidate estimated paths and then filtering the tracks in the spatio-temporal domain. Our approach permits to handle small and large displacements with improved accuracy and it is able to recover a trajectory after temporary occlusions. Especially useful for video editing applications, we attack the problem of graphic element insertion and video volume segmentation, together with a number of quantitative comparisons on ground-truth data with state-of-the-art approaches

Face flow

In this paper, we propose a method for the robust and efficient computation of multi-frame optical flow in an expressive sequence of facial images. We formulate a novel energy minimisation problem for establishing dense correspondences between a neutral template and every frame of a sequence. We exploit the highly correlated nature of human expressions by representing dense facial motion using a deformation basis. Furthermore, we exploit the even higher correlation between deformations in a given input sequence by imposing a low-rank prior on the coefficients of the deformation basis, yielding temporally consistent optical flow. Our proposed model-based formulation, in conjunction with the inverse compositional strategy and low-rank matrix optimisation that we adopt, leads to a highly efficient algorithm for calculating facial flow. As experimental evaluation, we show quantitative experiments on a challenging novel benchmark of face sequences, with dense ground truth optical flow provided by motion capture data. We also provide qualitative results on a real sequence displaying fast motion and occlusions. Extensive quantitative and qualitative comparisons demonstrate that the proposed method outperforms state-of-the-art optical flow and dense non-rigid registration techniques, whilst running an order of magnitude faster