Framework For Motion Segmentation Research Articles

A Multi-Scale Recurrent Framework for Motion Segmentation With Event Camera

A Multi-Scale Recurrent Framework for Motion Segmentation With Event Camera

What to Select: Pursuing Consistent Motion Segmentation from Multiple Geometric Models

Motion segmentation aims at separating motions of different moving objects in a video sequence. Facing the complicated real-world scenes, recent studies reveal that combining multiple geometric models would be a more effective way than just employing a single one. This motivates a new wave of model-fusion based motion segmentation methods. However, the vast majority of models of this kind merely seek consensus in spectral embeddings. We argue that a simple consensus might be insufficient to filter out the harmful information which is either unreliable or semantically unrelated to the segmentation task. Therefore, how to automatically select valuable patterns across multiple models should be regarded as a key challenge here. In this paper, we present a novel geometric-model-fusion framework for motion segmentation, which targets at constructing a consistent affinity matrix across all the geometric models. Specifically, it incorporates the structural information shared by affinity matrices to select those semantically consistent entries. Meanwhile, a multiplicative decomposition scheme is adopted to ensure structural consistency among multiple affinities. To solve this problem, an alternative optimization scheme is proposed, together with a proof of its global convergence. Experiments on four real-world benchmarks show the superiority of the proposed method.

A new fusion framework for motion segmentation in dynamic scenes

ABSTRACT Motion segmentation in dynamic scenes is currently widely dominated by parametric methods based on deep neural networks. The present study explores the unsupervised segmentation approach that can be used in the absence of training data to segment new videos. In particular, it tackles the task of dynamic texture segmentation. By automatically assigning a single class label to each region or group, this task consists of clustering into groups complex phenomena and characteristics which are both spatially and temporally repetitive. We present an effective fusion framework for motion segmentation in dynamic scenes (FFMS). This model is designed to merge different segmentation maps that contain multiple and weak quality regions in order to achieve a more accurate final result of segmentation. The diverse labelling fields required for the combination process are obtained by a simplified grouping scheme applied to an input video (on the basis of a three orthogonal planes: , and ). Experiments conducted on two challenging datasets (SynthDB and YUP++) show that, contrary to current motion segmentation approaches that either require parameter estimation or a training step, FFMS is significantly faster, easier to code, simple and has limited parameters.

Minimal Basis Subspace Representation: A Unified Framework for Rigid and Non-rigid Motion Segmentation

Motion segmentation and non-rigid structure from motion are two challenging computer vision problems that have attracted numerous research interests. While the previous works handle these two problems separately, we present a general motion segmentation framework in this paper for solving these two seemingly different problems in a unified manner. At the heart of our general motion segmentation framework is a model selection mechanism based on finding the minimal basis subspace representation, by seeking the joint sparse representation of the data matrix. However, such formulation is NP-hard and we solve the convex proxy instead. Unlike other compressive sensing related works, this convex proxy solution is insufficient for our problem. The convex relaxation artefacts and noise yield multiple subspace representations, making identification of the exact number of motion subspaces challenging. We solve for the right number of subspaces by transforming this problem into a Facility Location problem with global cost and solve the factor graph formulation using max product belief propagation message passing.

A novel framework for motion segmentation and tracking by clustering incomplete trajectories

In this paper, we present a framework for visual object tracking based on clustering trajectories of image key points extracted from an image sequence. The main contribution of our method is that the trajectories are automatically extracted from the image sequence and they are provided directly to a model-based clustering approach. In most other methodologies, the latter constitutes a difficult part since the resulting feature trajectories have a short duration, as the key points disappear and reappear due to occlusion, illumination, viewpoint changes and noise. We present here a sparse, translation invariant regression mixture model for clustering trajectories of variable length. The overall scheme is converted into a maximum a posteriori approach, where the Expectation–Maximization (EM) algorithm is used for estimating the model parameters. The proposed method detects the different objects in the input image sequence by assigning each trajectory to a cluster, and simultaneously provides their motion. Numerical results demonstrate the ability of the proposed method to offer more accurate and robust solutions in comparison with other tracking approaches, such as the mean shift tracker, the camshift tracker and the Kalman filter.

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation.

A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion

We present a novel framework for motion segmentation that combines the concepts of layer-based methods and feature-based motion estimation. We estimate the initial correspondences by comparing vectors of filter outputs at interest points, from which we compute candidate scene relations via random sampling of minimal subsets of correspondences. We achieve a dense, piecewise smooth assignment of pixels to motion layers using a fast approximate graphcut algorithm based on a Markov random field formulation. We demonstrate our approach on image pairs containing large inter-frame motion and partial occlusion. The approach is efficient and it successfully segments scenes with inter-frame disparities previously beyond the scope of layer-based motion segmentation methods. We also present an extension that accounts for the case of non-planar motion, in which we use our planar motion segmentation results as an initialization for a regularized Thin Plate Spline fit. In addition, we present applications of our method to automatic object removal and to structure from motion.