Real-Time Camera Pose Estimation for Sports Fields
- URL: http://arxiv.org/abs/2003.14109v1
- Date: Tue, 31 Mar 2020 11:27:33 GMT
- Title: Real-Time Camera Pose Estimation for Sports Fields
- Authors: Leonardo Citraro, Pablo M\'arquez-Neila, Stefano Savar\`e, Vivek
Jayaram, Charles Dubout, F\'elix Renaut, Andr\'es Hasfura, Horesh Ben
Shitrit, Pascal Fua
- Abstract summary: We propose a novel framework that combines accurate localization and robust identification of specific keypoints in the image.
Our algorithm exploits both the field lines and the players' image locations, assuming their ground plane positions to be given.
We will demonstrate its effectiveness on challenging soccer, basketball, and volleyball benchmark datasets.
- Score: 43.50671336931042
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Given an image sequence featuring a portion of a sports field filmed by a
moving and uncalibrated camera, such as the one of the smartphones, our goal is
to compute automatically in real time the focal length and extrinsic camera
parameters for each image in the sequence without using a priori knowledges of
the position and orientation of the camera. To this end, we propose a novel
framework that combines accurate localization and robust identification of
specific keypoints in the image by using a fully convolutional deep
architecture. Our algorithm exploits both the field lines and the players'
image locations, assuming their ground plane positions to be given, to achieve
accuracy and robustness that is beyond the current state of the art. We will
demonstrate its effectiveness on challenging soccer, basketball, and volleyball
benchmark datasets.
Related papers
- KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction [58.04846444985808]
This paper introduces KRONC, a novel approach aimed at inferring view poses by leveraging prior knowledge about the object to reconstruct and its representation through semantic keypoints.
With a focus on vehicle scenes, KRONC is able to estimate the position of the views as a solution to a light optimization problem targeting the convergence of keypoints' back-projections to a singular point.
arXiv Detail & Related papers (2024-09-09T08:08:05Z) - PnLCalib: Sports Field Registration via Points and Lines Optimization [16.278222277579655]
Camera calibration in broadcast sports videos presents numerous challenges for accurate sports field registration.
Traditional search-based methods depend on initial camera pose estimates, which can struggle in non-standard positions.
We propose an optimization-based calibration pipeline that leverages a 3D soccer field model and a predefined set of keypoints to overcome these limitations.
arXiv Detail & Related papers (2024-04-12T11:15:15Z) - VICAN: Very Efficient Calibration Algorithm for Large Camera Networks [49.17165360280794]
We introduce a novel methodology that extends Pose Graph Optimization techniques.
We consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step.
Our framework retains compatibility with traditional PGO solvers, but its efficacy benefits from a custom-tailored optimization scheme.
arXiv Detail & Related papers (2024-03-25T17:47:03Z) - Context-Aware 3D Object Localization from Single Calibrated Images: A
Study of Basketballs [1.809206198141384]
We present a novel method for 3D basketball localization from a single calibrated image.
Our approach predicts the object's height in pixels in image space by estimating its projection onto the ground plane within the image.
The 3D coordinates of the ball are then reconstructed by exploiting the known projection matrix.
arXiv Detail & Related papers (2023-09-07T11:14:02Z) - Progressively Optimized Local Radiance Fields for Robust View Synthesis [76.55036080270347]
We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video.
For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner.
For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window.
arXiv Detail & Related papers (2023-03-24T04:03:55Z) - CROSSFIRE: Camera Relocalization On Self-Supervised Features from an
Implicit Representation [3.565151496245487]
We use Neural Radiance Fields as an implicit map of a given scene and propose a camera relocalization tailored for this representation.
The proposed method enables to compute in real-time the precise position of a device using a single RGB camera, during its navigation.
arXiv Detail & Related papers (2023-03-08T20:22:08Z) - SPARF: Neural Radiance Fields from Sparse and Noisy Poses [58.528358231885846]
We introduce Sparse Pose Adjusting Radiance Field (SPARF) to address the challenge of novel-view synthesis.
Our approach exploits multi-view geometry constraints in order to jointly learn the NeRF and refine the camera poses.
arXiv Detail & Related papers (2022-11-21T18:57:47Z) - Sports Camera Pose Refinement Using an Evolution Strategy [0.4910937238451484]
We develop a neural network architecture for an edge or area-based segmentation of a sports field.
We implement the evolution strategy, which purpose is to refine extrinsic camera parameters given a single, segmented sports field image.
Experimental comparison with state-of-the-art camera pose refinement methods on real-world data demonstrates the superiority of the proposed algorithm.
arXiv Detail & Related papers (2022-11-03T20:57:51Z) - Keypoint-less Camera Calibration for Sports Field Registration in Soccer [11.374200381593267]
We introduce a differentiable objective function that is able to learn the camera pose and focal length from segment correspondences.
We propose a novel approach for 3D sports field registration from broadcast soccer images.
arXiv Detail & Related papers (2022-07-24T10:31:25Z) - ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving
Cameras in the Wild [57.37891682117178]
We present a robust dense indirect structure-from-motion method for videos that is based on dense correspondence from pairwise optical flow.
A novel neural network architecture is proposed for processing irregular point trajectory data.
Experiments on MPI Sintel dataset show that our system produces significantly more accurate camera trajectories.
arXiv Detail & Related papers (2022-07-19T09:19:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.