Publications & Submissions
- Sports Field Localization Via Deep Structured Models
, S. Fidler, R. Urtasun
To appear in Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, US, July 2017
In this work, we propose a novel way of efficiently localizing a sports field from a single broadcast image
image of the game. Related work in this area relies on manually annotating a few key frames and
extending the localization to similar images, or installing fixed specialized cameras in the
stadium from which the layout of the field can be obtained. In contrast, we formulate this problem
as a branch and bound inference in a Markov random field where an energy function is defined in
terms of semantic cues such as the field surface, lines and circles obtained from a deep semantic
segmentation network. Moreover, our approach is fully automatic and depends only on a single
image from the broadcast video of the game. We demonstrate the effectiveness of our method by
applying it to soccer and hockey.
[supp pdf (139 mb)
[soccer data (57 mb)
[Code to be released soon
- Periodic Solutions of a Singularly Perturbed Delay Differential Equation with Two State-Dependent Delays
A. R. Humphries, D. A. Bernucci, R. C. Calleja, N. Homayounfar
, M. Snarski
Journal of Dynamics and Differential Equations
Delay Differential Equations (DDEs) are a certain class of differential equations in which the rate of change of a
system depends not only on its current state, which is the case for ordinary differential equations, but also
on its state in the past. These equations arise naturally when modelling various biological and physical
phenomena and provide a glimpse of the often complex and chaotic dynamics of such systems.
In this paper, we study the periodic solutions of a certain class of DDEs.
- MCMC Clustering and Its Convergence Issues
, M. Asgharian, V. Partovi Nia
Contributed Poster in JSM
Bayesian clustering using MCMC sampling is a popular approach.
When a Markov chain Monte Carlo method is applied, the Markov chain
samples are used to approximate the posterior after the chain is converged.
When the data grouping is the concern, the convergence must be checked over
the allocation space. The convergence of a Markov chain is verified, often using
a trace plot, or using other common quantitative criteria mostly designed for a
continuous state space. However, data allocation is a very large unordered discrete
space and therefore the common convergence criteria is nontrivial to apply.
We monitor the convergence of a clustering chain by a convergence criterion
devised for the data allocation space.