The existing approaches for salient motion segmentation are unable to
explicitly learn geometric cues and often give false detections on prominent
static objects. We exploit multiview geometric constraints to avoid such
shortcomings. To handle the nonrigid background like a sea, we also propose a
robust fusion mechanism between motion and appearance-based features. We find
dense trajectories, covering every pixel in the video, and propose
trajectory-based epipolar distances to distinguish between background and
foreground regions. Trajectory epipolar distances are data-independent and can
be readily computed given a few features' correspondences between the images.
We show that by combining epipolar distances with optical flow, a powerful
motion network can be learned. Enabling the network to leverage both of these
features, we propose a simple mechanism, we call input-dropout. Comparing the
motion-only networks, we outperform the previous state of the art on DAVIS-2016
dataset by 5.2% in the mean IoU score. By robustly fusing our motion network
with an appearance network using the input-dropout mechanism, we also
outperform the previous methods on DAVIS-2016, 2017 and Segtrackv2 dataset.

利用多视角几何约束和轨迹极线距离识别背景和前景，结合光流和输入 - 丢失机制提出了一种有效的运动网络，与先前的方法相比在 DAVIS-2016、2017 和 Segtrackv2 数据集上均获得了更好的效果。