Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 4) JOINT GME AND OUTLIER DETECTION METHODS <s> A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing <s> BIB001 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 4) JOINT GME AND OUTLIER DETECTION METHODS <s> We present an algorithm for low-complexity global motion estimation, that works with block-coded video (e.g. MPEG-2). A superimposed global motion model is fitted to decoded P-frame motion vector fields, providing real-time performance. Therefore a robust M-estimator is applied since a lot of outliers have to be expected in the motion vector fields. The algorithm is compared to a state-of-the-art global motion estimator. The main characteristics of the global motion are captured accurately in most cases, although estimation may fail. We also report the integration of the estimator into a complete MPEG-7 system for search-and-retrieval of video based on motion characteristics. <s> BIB002 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 4) JOINT GME AND OUTLIER DETECTION 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> 4) JOINT GME AND OUTLIER DETECTION METHODS <s> Global motion estimation (GME) from motion vector (MV) field in compressed domain greatly reduces the complexity of conventional pixel-based GME. However, outlier MVs, caused by noise or foreground objects, may reduce the accuracy of MV-based GME. In this paper, we propose a cascade-of-rejectors approach for removing MV outliers to achieve efficient and accurate GME. Experimental results show that the proposed MV outlier rejection cascade significantly lowers the complexity MV-based GME, with an accuracy close to or better than state-of-the-art methods. <s> BIB004 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 4) JOINT GME AND OUTLIER DETECTION METHODS <s> In many content-based video processing systems, the presence of moving objects limits the accuracy of global motion estimation (GME). On the other hand, the inaccuracy of global motion parameter estimates affects the performance of motion segmentation. In this paper, we introduce a procedure for simultaneous object segmentation and GME from a coarsely sampled (i.e., block-based) motion vector (MV) field. The procedure starts with removing MV outliers from the MV field, and then performs GME to obtain an estimate of global motion parameters. Using these estimates, global motion is removed from the MV field, and moving region segmentation is performed on this compensated MV field. MVs in the moving regions are treated as outliers in the context of GME in the next round of processing. Iterating between GME and motion segmentation helps improve both GME and segmentation accuracy. Experimental results demonstrate the advantage of the proposed approach over state-of-the-art methods on both synthetic motion fields and MVs from real video sequences. <s> BIB005 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 4) JOINT GME AND OUTLIER DETECTION METHODS <s> Global motion estimation (GME) is an important technique in video processing, segmentation, and compression. In this correspondence, we propose a GME algorithm that offers improved accuracy especially in the presence of large-scale zoom. The proposed algorithm is derived from the observation that there exists an ambiguity between translation and zoom for simple geometric shapes such as lines, which may cause conventional GME to produce inaccurate estimates. Experimental results confirm the improved accuracy of the proposed method compared to the state-of-the-art methods. <s> BIB006
Since the global motion (inlier) estimation is often affected by object motions (outliers), several joint global motion estimation and object motion removal (outlier detection) methods were proposed. For example, a joint inlier estimation and motion segmentation method was proposed in BIB005 and BIB006 , which performs inlier estimation and outlier rejection simultaneously. These methods usually vary in the strategy of outlier detection. A regression scheme, which utilizes gradient descent (GD) BIB003 or least squares (LS) BIB002 , was applied to refine the camera motion model by excluding outliers iteratively. Outlier rejection filter BIB004 filters motion vectors explicitly by evaluating their similarity in a predefined window. RANSAC BIB001 , which is a statistical method, estimates the inlier through updating the probability of inlier iteratively.