Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) PARAMETRIC METHODS <s> Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In second-generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers. The more recent applications, such as content-based image/video retrieval and image/video composition, require that the segmented objects be semantically meaningful. Indeed, the recent multimedia standard MPEG-4 specifies that a video is composed of meaningful video objects. Although many segmentation techniques have been proposed in the literature, fully automatic segmentation tools for general applications are currently not achievable. This paper provides a review of this important and challenging area of segmentation of moving objects. We describe common approaches including temporal segmentation, spatial segmentation, and the combination of temporal-spatial segmentation. As an example, a complete segmentation scheme, which is an informative part of MPEG-4, is summarized. <s> BIB001 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) PARAMETRIC METHODS <s> In MPEG-4, global motion estimation (GME) and global motion compensation can improve the visual quality of local motion estimation/compensation. However, the computational complexity of GME is very high. Even the existing fast algorithm, FFRGMET, in the MPEG-4 OM3 software is not fast enough. In this paper, we propose a fast GME algorithm called APSGME. It uses motion vectors from the PMVFAST local motion estimation to perform early termination, foreground/background block-based segmentation, feature point selection. Experimental results shown that APSGME can achieve considerable speed-up over FFRGMET while improving the visual quality simultaneously. <s> BIB002 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) PARAMETRIC METHODS <s> In this paper, we propose a new approach for estimating global motions from a coarsely sampled motion vector field. The proposed method minimizes the fitting error between the input motion vectors and the motion vectors generated from the estimated motion model using the Newton-Raphson method with outlier rejections. Applications of the proposed method in video coding include fast global motion estimation for MPEG-4 Advanced Simple Profile (ASP) coding, MPEG-2 to MPEG-4 ASP transcoding, and error concealment of motion vectors. Simulation results and analyses are provided for the proposed method and applications, which show the accuracy and robustness of the proposed algorithm. <s> BIB003 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) PARAMETRIC METHODS <s> ABSTRACT Camera motion estimation is very important for indexing and retrieving video information. In this paper, we propose a robust camera motion estimation and classification algorithm. Our camera motion estimation algorithm consists of optical flow computation, iterative RANSAC (RANdom Sample Consensus) multiple motion estimation, and lone-term camera motion estimation through a shortest-path search. In this approach, we first estimate multiple global affine motions from the computed optical flow field for every frame in the video sequence. Then, the long-term camera motion is determined from searching a shortest path in a graph of cascaded nodes of global motions. After the camera motion is determined for the whole video, we apply an artificial neural network to classify the camera motion type. This neural network is trained from a large set of different types of camera motion data. We show accurate camera motion classification results through experiments on real videos. Keywords : camera motion classification, video analysis, optical flow computation, motion estimation, affine motion computation, RANSAC algorithm, artificial neural network. <s> BIB004 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) PARAMETRIC METHODS <s> Global motion compensation (GMC) is a coding tool introduced in MPEG-4 advanced simple profile (ASP); it can improve the coding efficiency on video sequences with global motion. Conventional pixel-domain iterative global motion estimation (GME) algorithms for estimating the perspective transform model parameters are very computationally demanding, and thus its applicability is restricted. A fast non-iterative GME algorithm is proposed for estimating the perspective transform global motion parameters from the motion vectors (MV) obtained from the block matching process. The proposed algorithm first fits multiple global motion models from groups of input MVs, then, a robust estimate is formed by a histogram-based postprocessing. Simulation results show the proposed algorithm can achieve superior coding performance while using much fewer computations compared to the iterative approaches. <s> BIB005 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) PARAMETRIC METHODS <s> In this paper, a novel algorithm for parametric camera motion estimation is introduced. More particularly, a novel stochastic vector field model is proposed, which can handle smooth motion patterns derived from long periods of stable camera motion and can also cope with rapid camera motion changes and periods when the camera remains still. The stochastic vector field model is established from a set of noisy measurements, such as motion vectors derived, e.g., from block matching techniques, in order to provide an estimation of the subsequent camera motion in the form of a motion vector field. A set of rules for a robust and online update of the camera motion model parameters is also proposed, based on the expectation maximization algorithm. The proposed model is embedded in a particle filters framework in order to predict the future camera motion based on current and prior observations. We estimate the subsequent camera motion by finding the optimum affine transform parameters so that, when applied to the current video frame, the resulting motion vector field to approximate the one estimated by the stochastic model. Extensive experimental results verify the usefulness of the proposed scheme in camera motion pattern classification and in the accurate estimation of the 2D affine camera transform motion parameters. Moreover, the camera motion estimation has been incorporated into an object tracker in order to investigate if the new schema improves its tracking efficiency, when camera motion and tracked object motion are combined. <s> BIB006 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) PARAMETRIC METHODS <s> In this paper, we propose an unsupervised segmentation algorithm for extracting moving regions from compressed video using global motion estimation (GME) and Markov random field (MRF) classification. First, motion vectors (MVs) are compensated from global motion and quantized into several representative classes, from which MRF priors are estimated. Then, a coarse segmentation map of the MV field is obtained using a maximum a posteriori estimate of the MRF label process. Finally, the boundaries of segmented moving regions are refined using color and edge information. The algorithm has been validated on a number of test sequences, and experimental results are provided to demonstrate its advantages over state-of-the-art methods. <s> BIB007 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) PARAMETRIC METHODS <s> Video has to be segmented into objects for content-based processing. A number of video object segmentation algorithms have been proposed such as semiautomatic and automatic. Semiautomatic methods adds burden to users and also not suitable for some applications. Automatic segmentation systems are still a challenge, although they are required by many applications. The proposed work aims at contributing to identify the gaps that are present in the current segmentation system and also to give the possible solutions to overcome those gaps so that the accurate and efficient video segmentation system can be developed. The proposed system aims to resolve the issue of uncovered background, Temporary poses and Global motion of background Keywords - Moving Object, Region based Segmentation, Block Matching, Global Motion Estimation and Compensation <s> BIB008
Generally speaking, motion in the image sequence is generated from motion of a camera and motion of individual objects. The former motion is refered as global motion while the latter is called as local motion. The estimation of camera motion is known as global motion estimation. According to the camera motion interpretation mentioned in Section 3, various global motion estimation models have been developed for this taks. These models are depicted by different number of parameters such as the six-parameter affine model, eight-parameter perspective model, eight-parameter quadratic model, and twelve-parameter parabolic model. The methods using parametric global motion models for camera motion estimation and compensation are refered as parametric methods BIB001 . Parametric models are constructed by using the physical structure of the scene and object motions. Two physical structure models used in parametric methods are usually planar surface or parabolic surface. They are reasonable for the approximation of practical structure of objects in natural scenes. The real object motions in the three dimensional space is usually modeled by a 3D affine motion model which is described by a translation vector T and a rotation matrix R by X = RX + T , where X and X represent objects points at time t and t + 1, respectively. Through combining the scene structure and motion constraints with one of the two geometry models: parallel and perspective, parametric models are defined as follows BIB001 : (1) Affine model: a six-parameter model corresponds to a planar surface under parallel projection: (2) Perspective model: an eight-parameter model corresponds to a planar surface under perspective projection: (3) Quadratic model: another eight-parameter model, which is generated by combining 3D object velocity model V , a planar surface and perspective projective geometry constraints: (4) Parabolic model: a twelve-parameter model corresponds to a parabolic surface under parallel projection: The affine model and the perspective model, which are two well-known camera motion models, have been widely used for camera motion estimation and compensation methods. Camera motions can be represented by either trajectory of key features or dense image motion field (such as optical flow). Considering these two camera motion representations, parametric methods can be further divided into two subgroups: feature based methods and dense motion field based methods. -Feature based Parametric Methods: Feature based methods use the motion of sparse key features to calculate the parameters of camera models (e.g. affine model, perspective model). Representation methods include Fu et al. BIB002 , Duan et al. 2008 [13] , and Yue-Meng Chen and Saeedi 2011 BIB007 . In BIB002 , a 6-parameter affine model is applied to describe the global motion estimation field. In  and BIB007 , a 8-parameter perspective model is used for camera motion estimation. The feature based methods usually need to extract and select good feature points from the scene first. They rely on a robust feature extraction and tracking algorithm, which is very difficult in complex natural scenes. -Dense Motion Field based Parametric Methods: Other than feature based methods, dense motion field based methods perform pixel-level motion estimation using image plane motion (optical flow) rather than key features. Similar to feature based methods, dense motion field based methods can also perform global motion estimation using a parametric model as described above BIB003 . Pixels that are consistent with the estimated model are supposed to be camera induced image motion, while others are supposed to be object induced image motions. Representative dense motion field based methods include Lai BIB004 , Nikitidis et al. BIB006 , Wagh et al. BIB008 , and Su and Sun BIB005 . In BIB004 , BIB006 , and BIB008 , the 6-parameter affine model is used while in BIB005 the 8-parameter perspective model is applied for the camera motion modeling based on dense motion field.