6,353 research outputs found
Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field Registration
The filming of sporting events projects and flattens the movement of athletes
in the world onto a 2D broadcast image. The pixel locations of joints in these
images can be detected with high validity. Recovering the actual 3D movement of
the limbs (kinematics) of the athletes requires lifting these 2D pixel
locations back into a third dimension, implying a certain scene geometry. The
well-known line markings of sports fields allow for the calibration of the
camera and for determining the actual geometry of the scene. Close-up shots of
athletes are required to extract detailed kinematics, which in turn obfuscates
the pertinent field markers for camera calibration. We suggest partial sports
field registration, which determines a set of scene-consistent camera
calibrations up to a single degree of freedom. Through joint optimization of 3D
pose estimation and camera calibration, we demonstrate the successful
extraction of 3D running kinematics on a 400m track. In this work, we combine
advances in 2D human pose estimation and camera calibration via partial sports
field registration to demonstrate an avenue for collecting valid large-scale
kinematic datasets. We generate a synthetic dataset of more than 10k images in
Unreal Engine 5 with different viewpoints, running styles, and body types, to
show the limitations of existing monocular 3D HPE methods. Synthetic data and
code are available at https://github.com/tobibaum/PartialSportsFieldReg_3DHPE.Comment: accept at "9th International Workshop on Computer Vision in Sports
(CVsports) at CVPR 2023
Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras
We propose a new method to estimate the 6-dof trajectory of a flying object
such as a quadrotor UAV within a 3D airspace monitored using multiple fixed
ground cameras. It is based on a new structure from motion formulation for the
3D reconstruction of a single moving point with known motion dynamics. Our main
contribution is a new bundle adjustment procedure which in addition to
optimizing the camera poses, regularizes the point trajectory using a prior
based on motion dynamics (or specifically flight dynamics). Furthermore, we can
infer the underlying control input sent to the UAV's autopilot that determined
its flight trajectory.
Our method requires neither perfect single-view tracking nor appearance
matching across views. For robustness, we allow the tracker to generate
multiple detections per frame in each video. The true detections and the data
association across videos is estimated using robust multi-view triangulation
and subsequently refined during our bundle adjustment procedure. Quantitative
evaluation on simulated data and experiments on real videos from indoor and
outdoor scenes demonstrates the effectiveness of our method
Effects of footwear variations on three-dimensional kinematics and tibial accelerations of specific movements in American football
American football is associated with a high rate of non-contact chronic injuries. Players are able to select from both high and low cut footwear. The aim of the current investigation was to examine the influence of high and low cut American football specific footwear on tibial accelerations and three-dimensional (3D) kinematics during three sport specific movements. Twelve male American football players performed three movements, run, cut and vertical jump whilst wearing both low and high cut footwear. 3D kinematics of the lower extremities were measured using an eight-camera motion analysis system alongside tibial acceleration parameters which were obtained using a shank mounted accelerometer. Tibial acceleration and 3D kinematic differences between the different footwear were examined using either repeated measures or Friedman’s ANOVA. Tibial accelerations were significantly greater in the low cut footwear in comparison to the high cut footwear for the run and cut movements. In addition, peak ankle eversion and tibial internal rotation parameters were shown to be significantly greater in the low cut footwear in the running and cutting movement conditions. The current study indicates that the utilization of low cut American football footwear for training/performance may place American footballers at increased risk from chronic injuries
TVCalib: Camera Calibration for Sports Field Registration in Soccer
Sports field registration in broadcast videos is typically interpreted as the
task of homography estimation, which provides a mapping between a planar field
and the corresponding visible area of the image. In contrast to previous
approaches, we consider the task as a camera calibration problem. First, we
introduce a differentiable objective function that is able to learn the camera
pose and focal length from segment correspondences (e.g., lines, point clouds),
based on pixel-level annotations for segments of a known calibration object.
The calibration module iteratively minimizes the segment reprojection error
induced by the estimated camera parameters. Second, we propose a novel approach
for 3D sports field registration from broadcast soccer images. Compared to the
typical solution, which subsequently refines an initial estimation, our
solution does it in one step. The proposed method is evaluated for sports field
registration on two datasets and achieves superior results compared to two
state-of-the-art approaches.Comment: Accepted for publication at WACV'2
Homography Estimation in Complex Topological Scenes
Surveillance videos and images are used for a broad set of applications,
ranging from traffic analysis to crime detection. Extrinsic camera calibration
data is important for most analysis applications. However, security cameras are
susceptible to environmental conditions and small camera movements, resulting
in a need for an automated re-calibration method that can account for these
varying conditions. In this paper, we present an automated camera-calibration
process leveraging a dictionary-based approach that does not require prior
knowledge on any camera settings. The method consists of a custom
implementation of a Spatial Transformer Network (STN) and a novel topological
loss function. Experiments reveal that the proposed method improves the IoU
metric by up to 12% w.r.t. a state-of-the-art model across five synthetic
datasets and the World Cup 2014 dataset.Comment: Will be published in Intelligent Vehicle Symposium 202
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