Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 2) MOTION PLUS SCENE STRUCTURE BASED METHODS <s> In this paper, a two-frame approach is presented for segmentation of independent moving objects in video along with estimation of ego-motion, independent object motion and reconstruction of the dynamic scene using intensity images. The proposed method utilizes the least median of squares in estimating ego-motion and parallax constraints for segmenting independently moving objects. A 3D structure for the static scene is also estimated using surface parallax. The motion of moving objects is estimated by first fitting a parametric flow model followed by subspace analysis. The algorithm works well for unconstrained translational motion of moving objects. <s> BIB001 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 2) MOTION PLUS SCENE STRUCTURE BASED METHODS <s> Extracting high-quality dynamic foreground layers from a video sequence is a challenging problem due to the coupling of color, motion, and occlusion. Many approaches assume that the background scene is static or undergoes the planar perspective transformation. In this paper, we relax these restrictions and present a comprehensive system for accurately computing object motion, layer, and depth information. A novel algorithm that combines different clues to extract the foreground layer is proposed, where a voting-like scheme robust to outliers is employed in optimization. The system is capable of handling difficult examples in which the background is nonplanar and the camera freely moves during video capturing. Our work finds several applications, such as high-quality view interpolation and video editing. <s> BIB002 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 2) MOTION PLUS SCENE STRUCTURE BASED METHODS <s> The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer, and even camouflage. In addition to all of this, the ability to detect motion is nearly instantaneous. While there has been much recent progress in motion segmentation, it still appears we are far from human capabilities. In this work, we derive from first principles a likelihood function for assessing the probability of an optical flow vector given the 2D motion direction of an object. This likelihood uses a novel combination of the angle and magnitude of the optical flow to maximize the information about how objects are moving differently. Using this new likelihood and several innovations in initialization, we develop a motion segmentation algorithm that beats current state-of-the-art methods by a large margin. We compare to five state-of-the-art methods on two established benchmarks, and a third new data set of camouflaged animals, which we introduce to push motion segmentation to the next level. <s> BIB003
Background subtraction is an important technology for background/foreground segmentation. However, high-quality background modeling is a very challenging task because of tightly coupled color, depth, and motion giving rise to a large 55970 VOLUME 6, 2018 FIGURE 4. The segmentation result of a probabilistic model based causal motion segmentation in crowd natural scenes BIB003 . number of variables and significant ambiguity in computation and thus make the segmentation result unsatisfactory BIB002 . More and more researchers find that just relying on the color or texture information of image sequences is not enough for bilayaer (foreground and background) segmentation. We need to take into account both the motion and scene structure information for both background modeling and foreground segmentation. Thus, several motion plus scene structure based methods have been proposed in BIB001 -  . For example, Agrawal and Chellappa BIB001 introduced an independently moving object segmentation method based on an iterative algorithm, which segments objects moving independently under the pure translation and refine and update a coarse depth map the scene under unconstrained camera motions (camera translation or rotation). Zhang et al. BIB002 proposed a robust bilayer moving object segmentation and depth estimation system, which is robust for accurately computing object motion, layer, and depth information under difficult situations. Bideau and Learned-Miller BIB003 developed a probabilistic moving object detection model for causal motion segmentation in crowd natural scenes. Through deriving a novel likelihood function for assessing the probability of moving object in optical flow field given the 3D motion direction of an object, this method has become the state-ofthe-art method in 2016.