Tracking a table tennis ball for umpiring purposes using a multi-agent system

Tracking a table tennis ball for umpiring purposes is a challenging task as, in real-match scenarios, the ball travels fast and can become occluded or merged with other background objects. This paper presents the design of a multi-view based tracking system that can overcome the challenges of tracking a ball in real match sequences. The system has been tested on a complete table tennis rally and the results are very promising. The system is able to continuously track the ball with only marginal variations in detection. Furthermore, the initialization of the multi-camera system means it is both a portable and cost-effective solution for umpiring purposes

Tracking a table tennis ball for umpiring purposes

This study investigates tracking a table-tennis ball rapidly from video captured using low-cost equipment for umpiring purposes. A number of highly efficient algorithms have been developed for this purpose. The proposed system was tested using sequences capture from real match scenes. The preliminary results of experiments show that accurate and rapid tracking can be achieved even under challenging conditions, including occlusion and colour merging. This work can contribute to the development of an automatic umpiring system and also has the potential to provide amateur users open access to a detection tool for fast-moving, small, round objects

A Novel Multi-View Table Tennis Umpiring Framework

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status. While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies. In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development

Tracking Table Tennis Balls in Real Match Scenes for Umpiring Applications

Judging the legitimacy of table tennis services presents many challenges where technology can be judiciously applied to enhance decision-making. This paper presents a purpose-built system to automatically detect and track the ball during table-tennis services to enable precise judgment over their legitimacy in real-time. The system comprises a suite of algorithms which adaptively exploit spatial and temporal information from real match video sequences, which are generally characterised by high object motion, allied with object blurring and occlusion. Experimental results on a diverse set of table-tennis test sequences corroborate the system performance in facilitating consistently accurate and efficient decision-making over the validity of a service

Identifying table tennis balls from real match scenes using image processing and artificial intelligence techniques

Table tennis is a fast sport and it is very difficult for a normal human being to manage accurate umpiring, especially in services (serves), which usually take less than a second to complete. The umpire needs to make over 30 observations and makes a judgment before or soon after the service is complete. This is a complex task and the author believes the employment of image processing and artificial intelligence (AI) technologies could aid the umpire to evaluating services more accurately. The aim of this research is to develop an intelligent system which is able to identify and track the location of the ball from live video images and evaluate the service according to the service rules. In this paper, the discussion is focused on the development of techniques for identifying a table tennis ball from match scenes. These techniques formed the basis of the ball detection system. Artificial neural networks (ANN) have been designed and applied to further the accuracy of the detection system. The system has been tested on still images taken at real match scenes and the preliminary results are very promising. Almost all the balls from the images have been correctly identified. The system has been further tested on some video images and the preliminary result is also very encouraging. It shows the system could tolerate the poorer quality of video images. This paper also discusses the idea of employing multiple cameras for improving accuracy. A multi-agent system is proposed because it is known to be able to coordinate and manage the flow of information more effectively

High-motion table tennis ball tracking for umpiring applications

Table-tennis umpiring presents many challenges where technology can be judiciously applied to enhance decision-making, especially in the service facet of the game. This paper presents a system to automatically detect and track the ball during table-tennis services to enable precise judgment over their legitimacy. The system comprises a suite of algorithms that adaptively exploit spatial and temporal information from real match videos, which are generally characterized by high object motion, allied with object blurring and occlusion. Experimental results on various table-tennis test videos corroborate the system performance in facilitating accurate and efficient decision-making over the validity of a service

Multiview System for Tracking a Fast Moving Object Against Complex Backgrounds

Tracking the real world coordinate of a fast moving object against a complex background is very challenging. When designing a multi-view system for this purpose, one key consideration is the arrangement of the cameras such that the object can be constantly and accurately tracked. This paper discusses a novel cameras arrangement, which can provide redundancy for fault tolerance, yet do not require installing more cameras nor relying aerial views of the scene. Using a table tennis match as example, experiment results show that the multi-view system with this cameras arrangement has a promising potential for tracking a table tennis ball in a real match scene

Two stream network for stroke detection in table tennis

This paper presents a table tennis stroke detection method from videos. Themethod relies on a two-stream Convolutional Neural Network processing inparallel the RGB Stream and its computed optical flow. The method has beendeveloped as part of the MediaEval 2021 benchmark for the Sport task. Ourcontribution did not outperform the provided baseline on the test set but hasperformed the best among the other participants with regard to the mAP metric.<br

A multi-view automatic table tennis umpiring framework

This paper presents a low-cost, portable, multi-view table tennis umpiring framework, as a viable alternative to the current expensive systems which are almost exclusively restricted to elite professional sports. Table tennis has been selected as the sport to evaluate this framework primarily because it comprises many different complex match elements, including the service, return and rally elements, which are governed by a strict set of regulations which need to be umpired. The aim is to develop novel methods to analyse and judge the legality of such key match facets, with ball detection and tracking in video frames being integral to reliably and accurately determining the ball’s position and flight path during rallies. While a low-cost option is attractive and offers several benefits, it is a technically challenging problem due to the small number and generally low-resolution cameras that are used. A novel multi-view camera setup and multi-agent system (MAS) framework is presented, which comprises computationally lightweight agents which detect and track the table tennis ball, create a 3D representation of the flight path of the ball, predict the ball’s trajectory, and identify and analyse key facets in a table tennis rally. The MAS correctly detects all state transitions in seven test table tennis sequences with minimal latency and while the processing rate of a standard computer may be unable to analyse long rallies in real time, the potential of running the MAS on a parallel architecture is a propitious alternative. The MAS is also scalable, enabling additional camera pairs to be deployed to achieve enhanced accuracy and coverage. The framework affords the potential to reform the way matches are umpired, especially for amateur players, providing an economic and objective manner of dispute resolution, while the multi-view facility is extendible to other relevant ball-based sports. The ball flight path analysis mechanism can be exploited as a valuable training tool for skill development