Spectral Methods for 3-D Motion Segmentation of Sparse Scene-Flow

International audienceThe progress in the acquisition of 3-D data from multicamera set-ups has opened the way to a new way of loking at motion analysis. This paper proposes a solution to the motion segmentation in the context of sparse scene flow. In particular, our interest focuses on the disassociation of motions belonging to different rigid objects, starting from the 3-D trajectories of features lying on their surfaces. We analyze these trajectories and propose a representation suitable for defining robust-pairwise similarity measures between trajectories and handling missing data. The motion segmentation is treated as graph multi-cut problem, and solved with spectral clustering techniques (two algorithms are presented). Experiments are done over simulated and real data in the form of sparse scene-flow; we also evaluate the results on trajectories from motion capture data. A discussion is provided on the results for each algorithm, the parameters and the possible use of these results in motion analysis

Spectral clustering of linear subspaces for motion segmentation

International audienceThis paper studies automatic segmentation of multiple motions from tracked feature points through spectral embedding and clustering of linear subspaces. We show that the dimension of the ambient space is crucial for separability, and that low dimensions chosen in prior work are not optimal. We suggest lower and upper bounds together with a data-driven procedure for choosing the optimal ambient dimension. Application of our approach to the Hopkins155 video benchmark database uniformly outperforms a range of state-of-the-art methods both in terms of segmentation accuracy and computational speed

Robust motion segmentation by spectral clustering

Multibody motion segmentation is important in many computer vision tasks. One way to solve this problem is factorization. But practically segmentation is difficult since the shape interaction matrix is contaminated by noise. This paper presents a novel approach to robustly segment multiple moving objects by spectral clustering. We introduce two new affinity matrixes. One is based on the shape interaction matrix and the other one is based on the motion trajectory. By computing the sensitivities of the larger eigenvalues of a related Markov transition matrix with respect to perturbations in the affinity matrix, we improve the piecewise constant eigenvectors condition dramatically. The feature points are mapped into a low dimensional subspace and clustered in this subspace using a graph spectral approach. This makes clustering much more reliable and robust, which we confirm with experiments.