[Paper Review] Design and Implementation of A Soccer Ball Detection System with Multiple Cameras

Lei Li, Tianfang Zhang+2 more|arXiv (Cornell University)|Jan 31, 2023

Video Analysis and Summarization4 citations

TL;DR

This paper presents a real-time 3D soccer ball detection system using 36 cameras and deep learning, leveraging YOLO-based object detection, multi-camera fusion, and bundle adjustment for precise 3D localization. The system achieves high accuracy and real-time performance despite challenges like occlusion, motion, and lighting variations, with successful deployment in live sports broadcasting applications.

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.

Motivation & Objective

• Address the challenge of real-time 3D detection of small, fast-moving objects like soccer balls in complex sports environments.
• Overcome limitations of traditional 3D detection systems, such as high cost, low precision, and poor real-time performance.
• Develop a scalable, reusable system for live broadcast applications in sports like soccer and basketball.
• Ensure robustness under real-world conditions, including occlusion, motion blur, and uneven illumination.
• Integrate multi-camera vision with deep learning to achieve accurate, real-time 3D tracking of the ball using GPU-accelerated processing.

Proposed method

• Employ a YOLO-based deep convolutional neural network (YOLOv3) for real-time 2D ball detection in single-camera views.
• Apply model compression and optimization techniques to maintain high accuracy while enabling real-time inference.
• Use bundle adjustment to triangulate 2D detections across multiple cameras and compute accurate 3D positions of the ball.
• Implement an adaptive video detection framework to handle high-volume video streams from 36 cameras.
• Leverage GPU acceleration for data pre-processing and real-time inference to meet low-latency requirements.
• Design a modular system architecture with dedicated modules for data pre-processing, single-camera detection, and multi-camera fusion.

Figure 1: Convolutional neural network extracted features

Experimental results

Research questions

• RQ1 How can a real-time, high-precision 3D soccer ball detection system be designed using multiple cameras and deep learning?
• RQ2 What techniques can effectively handle occlusion, motion blur, and illumination variation in dynamic sports scenes?
• RQ3 How can multi-camera 2D detections be fused to achieve accurate 3D localization of a small object (ball) in real time?
• RQ4 What level of model compression and optimization is required to maintain accuracy while enabling real-time inference on a large-scale camera setup?
• RQ5 To what extent is the proposed system reusable and scalable for other sports or tracking applications?

Key findings

• The system successfully detects and tracks the soccer ball in 3D with high accuracy under challenging conditions such as occlusion and motion.
• Model compression and optimization enabled real-time inference while preserving detection accuracy, crucial for live broadcast applications.
• Bundle adjustment effectively fused multi-camera 2D detections to achieve precise 3D ball localization, even with small target size (~2×10⁻⁵ of image area).
• The system demonstrated robust performance across diverse lighting and motion conditions, validating its real-world applicability.
• The framework is reusable and translatable to other sports, such as basketball, due to its modular and scalable design.
• The system has been deployed commercially, confirming its practicality and real-time performance in live sports environments.

Figure 2: Convolutional neural network diagram.