TennisSense: a multi-sensory approach to performance analysis in tennis

The TennisSense Project, that is run in collaboration with Tennis Ireland, aims to create the infrastructure required to digitally capture physical, tactical and physiological data from tennis players in order to assist in their coaching and improved performance. This study examined the potential for using Wireless Inertial Monitoring Units (WIMU) to model the biomechanical aspects of the tennis stroke and for developing coaching tools that utilise this information. There is significant evidence in the current literature that the ability to accurately capture and model the accelerations, angular velocities and orientations involved in the tennis stroke could facilitate a major step forward in the application of biomechanics to tennis coachin

TennisSense: a platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface

An automatic visual analysis system for tennis

This article presents a novel video analysis system for coaching tennis players of all levels, which uses computer vision algorithms to automatically edit and index tennis videos into meaningful annotations. Existing tennis coaching software lacks the ability to automatically index a tennis match into key events, and therefore, a coach who uses existing software is burdened with time-consuming manual video editing. This work aims to explore the effectiveness of a system to automatically detect tennis events. A secondary aim of this work is to explore the bene- fits coaches experience in using an event retrieval system to retrieve the automatically indexed events. It was found that automatic event detection can significantly improve the experience of using video feedback as part of an instructional coaching session. In addition to the automatic detection of key tennis events, player and ball movements are automati- cally tracked throughout an entire match and this wealth of data allows users to find interesting patterns in play. Player and ball movement information are integrated with the automatically detected tennis events, and coaches can query the data to retrieve relevant key points during a match or analyse player patterns that need attention. This coaching software system allows coaches to build advanced queries, which cannot be facilitated with existing video coaching solutions, without tedious manual indexing. This article proves that the event detection algorithms in this work can detect the main events in tennis with an average precision and recall of 0.84 and 0.86, respectively, and can typically eliminate man- ual indexing of key tennis events

Automatic camera selection for activity monitoring in a multi-camera system for tennis

In professional tennis training matches, the coach needs to be able to view play from the most appropriate angle in order to monitor players' activities. In this paper, we describe and evaluate a system for automatic camera selection from a network of synchronised cameras within a tennis sporting arena. This work combines synchronised video streams from multiple cameras into a single summary video suitable for critical review by both tennis players and coaches. Using an overhead camera view, our system automatically determines the 2D tennis-court calibration resulting in a mapping that relates a player's position in the overhead camera to their position and size in another camera view in the network. This allows the system to determine the appearance of a player in each of the other cameras and thereby choose the best view for each player via a novel technique. The video summaries are evaluated in end-user studies and shown to provide an efficient means of multi-stream visualisation for tennis player activity monitoring

Automatic annotation of tennis games: An integration of audio, vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level

CELEBRITY ENDORSEMENT IN ADVERTISING

Being an individual sport, tennis often deals with a specific category of sponsoring: the endorsement. Tennis players and business organizations seek each other in order to sign partnerships from which both parties hope to win. Endorsements are among the most profitable income sources for professional athletes. By acting as ambassadors for their sponsors and by cedeing them the image usage rights, tennis players earn a lot. But, through the image association with the athlete, the sponsor also wins, by adding value to his image capital. This papers researches the particularities of tennis endorsements using the case of Roger Federer, one of the best tennis players in history and one of the best business partners in the world of sports for sponsoring companies. Thanks to his succes, Federer reached a level where not less than ten partners sponsor him yearly. The most interesting of these partnerships are presented in this paper.celebrity endorsement, sponsoring, sports marketing, tennis

Evaluation of Archaeological Material from the Little Church of La Villita Property, San Antonio, Texas

On June 25th, 1993, Dale Bransford of the San Antonio Parks and Recreation Department brought several bone fragments to the Center for Archaeological Research (CAR) of the University of Texas at San Antonio (UTSA). These bones had been discovered by workmen installing a French drain along the east side of the Little Church of La V ill ita in downtown San Antonio. Inspection identified the bones as human, so CAR staff members visited the site to investigate and collect other remains. No excavation was attempted. Subsequent skeletal analysis revealed the presence of at least two individuals in the collection. Because of the absence of diagnostic artifacts, a sample of bone was sent for accelerator dating. The resulting date of 380 ± 60 B.P. (Beta-67731 AMS-9901) suggests that these remains are those of at least two late prehistoric or proto historic occupants of south central Texas. The Delta13 C value of the bone indicates a dietary regime which emphasized C4-based food sources, in this case probably bison

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

Image segmentation and feature extraction for recognizing strokes in tennis game videos

This paper addresses the problem of recognizing human actions from video. Particularly, the case of recognizing events in tennis game videos is analyzed. Driven by our domain knowledge, a robust player segmentation algorithm is developed real video data. Further, we introduce a number of novel features to be extracted for our particular application. Different feature combinations are investigated in order to find the optimal one. Finally, recognition results for different classes of tennis strokes using automatic learning capability of Hidden Markov Models (HMMs) are presented. The experimental results demonstrate that our method is close to realizing statistics of tennis games automatically using ordinary TV broadcast videos