Design and Implementation of A Soccer Ball Detection System with
Multiple Cameras
- URL: http://arxiv.org/abs/2302.00123v1
- Date: Tue, 31 Jan 2023 22:04:53 GMT
- Title: Design and Implementation of A Soccer Ball Detection System with
Multiple Cameras
- Authors: Lei Li, Tianfang Zhang, Zhongfeng Kang, Wenhan Zhang
- Abstract summary: This paper designed and implemented football detection system under multiple cameras for the detection and capture of targets in real-time matches.
The main work mainly consists of three parts, football detector, single camera detection, and multi-cameras detection.
By testing the system, it shows that the system can accurately detect and capture the moving targets in 3D.
- Score: 15.399112952297335
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The detection of small and medium-sized objects in three dimensions has
always been a frontier exploration problem. This technology has a very wide
application in sports analysis, games, virtual reality, human animation and
other fields. The traditional three-dimensional small target detection
technology has the disadvantages of high cost, low precision and inconvenience,
so it is difficult to apply in practice. With the development of machine
learning and deep learning, the technology of computer vision algorithms is
becoming more mature. Creating an immersive media experience is considered to
be a very important research work in sports.
The main work is to explore and solve the problem of football detection under
the multiple cameras, aiming at the research and implementation of the live
broadcast system of football matches. Using multi cameras detects a target ball
and determines its position in three dimension with the occlusion, motion, low
illumination of the target object.
This paper designed and implemented football detection system under multiple
cameras for the detection and capture of targets in real-time matches. The main
work mainly consists of three parts, football detector, single camera
detection, and multi-cameras detection. The system used bundle adjustment to
obtain the three-dimensional position of the target, and the GPU to accelerates
data pre-processing and achieve accurate real-time capture of the target. By
testing the system, it shows that the system can accurately detect and capture
the moving targets in 3D.
In addition, the solution in this paper is reusable for large-scale
competitions, like basketball and soccer. The system framework can be well
transplanted into other similar engineering project systems. It has been put
into the market.
Related papers
- Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data [68.18735997052265]
We propose a balanced approach that combines the advantages of monocular and point cloud-based 3D detection.
Our method requires only a small number of 3D points, that can be obtained from a low-cost, low-resolution sensor.
The accuracy of 3D detection improves by 20% compared to the state-of-the-art monocular detection methods.
arXiv Detail & Related papers (2024-04-10T03:54:53Z) - Joint object detection and re-identification for 3D obstacle
multi-camera systems [47.87501281561605]
This research paper introduces a novel modification to an object detection network that uses camera and lidar information.
It incorporates an additional branch designed for the task of re-identifying objects across adjacent cameras within the same vehicle.
The results underscore the superiority of this method over traditional Non-Maximum Suppression (NMS) techniques.
arXiv Detail & Related papers (2023-10-09T15:16:35Z) - 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) - MonoNext: A 3D Monocular Object Detection with ConvNext [69.33657875725747]
This paper introduces a new Multi-Tasking Learning approach called MonoNext for 3D Object Detection.
MonoNext employs a straightforward approach based on the ConvNext network and requires only 3D bounding box data.
In our experiments with the KITTI dataset, MonoNext achieved high precision and competitive performance comparable with state-of-the-art approaches.
arXiv Detail & Related papers (2023-08-01T15:15:40Z) - Graph-Based Multi-Camera Soccer Player Tracker [1.6244541005112743]
The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field.
The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions.
Our method focuses on individual player dynamics and interactions between neighborhood players to improve tracking performance.
arXiv Detail & Related papers (2022-11-03T20:01:48Z) - A Simple Baseline for Multi-Camera 3D Object Detection [94.63944826540491]
3D object detection with surrounding cameras has been a promising direction for autonomous driving.
We present SimMOD, a Simple baseline for Multi-camera Object Detection.
We conduct extensive experiments on the 3D object detection benchmark of nuScenes to demonstrate the effectiveness of SimMOD.
arXiv Detail & Related papers (2022-08-22T03:38:01Z) - Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object
Tracking [114.16306938870055]
We propose a coordinate-aligned multi-camera collaboration system for AMOT.
In our approach, we regard each camera as an agent and address AMOT with a multi-agent reinforcement learning solution.
Our system achieves a coverage of 71.88%, outperforming the baseline method by 8.9%.
arXiv Detail & Related papers (2022-02-22T13:28:40Z) - Analysis of voxel-based 3D object detection methods efficiency for
real-time embedded systems [93.73198973454944]
Two popular voxel-based 3D object detection methods are studied in this paper.
Our experiments show that these methods mostly fail to detect distant small objects due to the sparsity of the input point clouds at large distances.
Our findings suggest that a considerable part of the computations of existing methods is focused on locations of the scene that do not contribute with successful detection.
arXiv Detail & Related papers (2021-05-21T12:40:59Z) - Three-dimensional Human Tracking of a Mobile Robot by Fusion of Tracking
Results of Two Cameras [0.860255319568951]
OpenPose is used for human detection.
A new stereo vision framework is proposed to cope with the problems.
The effectiveness of the proposed framework and the method is verified through target-tracking experiments.
arXiv Detail & Related papers (2020-07-03T06:46:49Z) - Multimodal and multiview distillation for real-time player detection on
a football field [31.355119048749618]
We develop a system that detects players from a unique cheap and wide-angle fisheye camera assisted by a single narrow-angle thermal camera.
We show that our solution is effective in detecting players on the whole field filmed by the fisheye camera.
arXiv Detail & Related papers (2020-04-16T09:16:20Z)
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.