Ball 3D localization from a single calibrated image
- URL: http://arxiv.org/abs/2204.00003v1
- Date: Wed, 30 Mar 2022 19:38:14 GMT
- Title: Ball 3D localization from a single calibrated image
- Authors: Gabriel Van Zandycke and Christophe De Vleeshouwer
- Abstract summary: We propose to address the task on a single image by estimating ball diameter in pixels and use the knowledge of real ball diameter in meters.
This approach is suitable for any game situation where the ball is (even partly) visible.
validations on 3 basketball datasets reveals that our model gives remarkable predictions on ball 3D localization.
- Score: 1.2891210250935146
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Ball 3D localization in team sports has various applications including
automatic offside detection in soccer, or shot release localization in
basketball. Today, this task is either resolved by using expensive multi-views
setups, or by restricting the analysis to ballistic trajectories. In this work,
we propose to address the task on a single image from a calibrated monocular
camera by estimating ball diameter in pixels and use the knowledge of real ball
diameter in meters. This approach is suitable for any game situation where the
ball is (even partly) visible. To achieve this, we use a small neural network
trained on image patches around candidates generated by a conventional ball
detector. Besides predicting ball diameter, our network outputs the confidence
of having a ball in the image patch. Validations on 3 basketball datasets
reveals that our model gives remarkable predictions on ball 3D localization. In
addition, through its confidence output, our model improves the detection rate
by filtering the candidates produced by the detector. The contributions of this
work are (i) the first model to address 3D ball localization on a single image,
(ii) an effective method for ball 3D annotation from single calibrated images,
(iii) a high quality 3D ball evaluation dataset annotated from a single
viewpoint. In addition, the code to reproduce this research is be made freely
available at https://github.com/gabriel-vanzandycke/deepsport.
Related papers
- Enhancing Soccer Camera Calibration Through Keypoint Exploitation [0.0]
This paper introduces a multi-stage pipeline that addresses the challenge of obtaining a sufficient number of high-quality point pairs.
Our approach significantly increases the number of usable points for calibration by exploiting line-line and line-conic intersections, points on the conics, and other geometric features.
We evaluated our approach on the largest football broadcast camera calibration dataset available, and secured the top position in the SoccerNet Camera Challenge 2023.
arXiv Detail & Related papers (2024-10-09T20:01:14Z) - 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
3DiffTection is a state-of-the-art method for 3D object detection from single images.
We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image.
We further train the model on target data with detection supervision.
arXiv Detail & Related papers (2023-11-07T23:46:41Z) - 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) - Deep-Learning-Based Computer Vision Approach For The Segmentation Of
Ball Deliveries And Tracking In Cricket [4.021584094339975]
This paper presents an approach to segment and extract video shots in which only the ball is being delivered.
Object detection models are applied to reach a high level of accuracy in terms of correctly extracting video shots.
Ball tracking in these video shots is also done using a separate RetinaNet model as a sample of the usefulness of the proposed dataset.
arXiv Detail & Related papers (2022-11-22T04:55:58Z) - An Empirical Study of Pseudo-Labeling for Image-based 3D Object
Detection [72.30883544352918]
We investigate whether pseudo-labels can provide effective supervision for the baseline models under varying settings.
We achieve 20.23 AP for moderate level on the KITTI-3D testing set without bells and whistles, improving the baseline model by 6.03 AP.
We hope this work can provide insights for the image-based 3D detection community under a semi-supervised setting.
arXiv Detail & Related papers (2022-08-15T12:17:46Z) - Object-Based Visual Camera Pose Estimation From Ellipsoidal Model and
3D-Aware Ellipse Prediction [2.016317500787292]
We propose a method for initial camera pose estimation from just a single image.
It exploits the ability of deep learning techniques to reliably detect objects regardless of viewing conditions.
Experiments prove that the accuracy of the computed pose significantly increases thanks to our method.
arXiv Detail & Related papers (2022-03-09T10:00:52Z) - Tracking People by Predicting 3D Appearance, Location & Pose [78.97070307547283]
We first lift people to 3D from a single frame in a robust way.
As we track a person, we collect 3D observations over time in a tracklet representation.
We use these models to predict the future state of the tracklet.
arXiv Detail & Related papers (2021-12-08T18:57:15Z) - Efficient Golf Ball Detection and Tracking Based on Convolutional Neural
Networks and Kalman Filter [15.899498333913975]
An efficient real-time approach is proposed by exploiting convolutional neural networks (CNN) based object detection and a Kalman filter based prediction.
The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection.
In order to train the detection models and test the tracking algorithm, a collection of golf ball dataset is created and annotated.
arXiv Detail & Related papers (2020-12-17T04:55:27Z) - Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image.
Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space.
We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step.
This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
arXiv Detail & Related papers (2020-08-31T17:10:48Z) - Kinematic 3D Object Detection in Monocular Video [123.7119180923524]
We propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization.
We achieve state-of-the-art performance on monocular 3D object detection and the Bird's Eye View tasks within the KITTI self-driving dataset.
arXiv Detail & Related papers (2020-07-19T01:15:12Z) - ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object
Detection [69.68263074432224]
We present a novel framework named ZoomNet for stereo imagery-based 3D detection.
The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes.
To further exploit the abundant texture cues in RGB images for more accurate disparity estimation, we introduce a conceptually straight-forward module -- adaptive zooming.
arXiv Detail & Related papers (2020-03-01T17:18:08Z)
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.