12 research outputs found
Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation
Minimum cost lifted multicut problem is a generalization of the multicut problem and is a means to optimizing a decomposition of a graph w.r.t. both positive and negative edge costs. Its main advantage is that multicut-based formulations do not require the number of components given a priori; instead, it is deduced from the solution. However, the standard multicut cost function is limited to pairwise relationships between nodes, while several important applications either require or can benefit from a higher-order cost function, i.e. hyper-edges. In this paper, we propose a pseudo-boolean formulation for a multiple model fitting problem. It is based on a formulation of any-order minimum cost lifted multicuts, which allows to partition an undirected graph with pairwise connectivity such as to minimize costs defined over any set of hyper-edges. As the proposed formulation is NP-hard and the branch-and-bound algorithm is too slow in practice, we propose an efficient local search algorithm for inference into resulting problems. We demonstrate versatility and effectiveness of our approach in several applications: geometric multiple model fitting, homography and motion estimation, motion segmentation
Five pounds per hour The case for a fair minimum wage: Irish Congress of Trade Unions submission to the National Minimum Wage Commission
SIGLEAvailable from British Library Document Supply Centre-DSC:98/11700 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
ArtTrack: Articulated Multi-Person Tracking in the Wild
In this paper we propose an approach for articulated tracking of multiple
people in unconstrained videos. Our starting point is a model that resembles
existing architectures for single-frame pose estimation but is substantially
faster. We achieve this in two ways: (1) by simplifying and sparsifying the
body-part relationship graph and leveraging recent methods for faster
inference, and (2) by offloading a substantial share of computation onto a
feed-forward convolutional architecture that is able to detect and associate
body joints of the same person even in clutter. We use this model to generate
proposals for body joint locations and formulate articulated tracking as
spatio-temporal grouping of such proposals. This allows to jointly solve the
association problem for all people in the scene by propagating evidence from
strong detections through time and enforcing constraints that each proposal can
be assigned to one person only. We report results on a public MPII Human Pose
benchmark and on a new MPII Video Pose dataset of image sequences with multiple
people. We demonstrate that our model achieves state-of-the-art results while
using only a fraction of time and is able to leverage temporal information to
improve state-of-the-art for crowded scenes.Comment: Accepted to CVPR 201