A query language for exploratory analysis of video-based tracking data in padel matches

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of- the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.Postprint (published version

A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.This research was funded by the Spanish Ministry of Science and Innovation and FEDER funds, grant number PID2021-122136OB-C21, MCIN/AEI/10.13039/501100011033/FEDER, UE

Tracking of moving athlete from video sequences using fower pollination algorithm

Performance analysis, as related to sport, is a process underpinned by a systematic analysis of information, to accelerate the performance of athletes through crafted focused practice session based on the obtained analysis. Quantifcation of athlete performance profle using sports video has thus been put forward, where the athlete tracking in such video-based analysis is one of the critical elements for the success of an object tracking system. In this study, for the frst time the fower pollination algorithm (FPA) is utilised to track the motion of the moving athlete from the sports video. Initially, a search window with the attributes of centroid coordinates of the moving athlete, width and length of the search window is used to represent the current position of the athlete. Subsequently, the hue, saturation and value (HSV) histogram of the region within the search window is evaluated. In the consecutive frame, several potential positions of the athlete are identifed, and the Bhattacharyya distance between the HSV histogram of the athlete in the previous frame and the potential position in the current frame is calculated. Since the FPA attempts to maximise the similarity of both histograms, intuitively, the current position of the moving athlete should be only slightly diferent than his previous position. The comparative analysis shows that the FPA is comparable with other competing algorithms in terms of detection rate, tracking accuracy and processing time

The role of motion analysis in elite soccer

The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game especially due to the increased physical demands of match-play. The monitoring of players’ work-rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university- based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, identify practical implications of the established body of knowledge, while highlighting areas that require further exploration

BIOMECHANICS FOR OLYMPIC SPORTS

INTRODUCTION Since the mid-1980s, Sports Science has been represented in various organizational charts as part of the U. S. Olympic Committee. Although originally located at the Olympic Training Center in Colorado Springs (CSOTC) the Sport Science and Technology Division now has staff members at the two other training centers in Lake Placid, N.Y. (LPOTC) and Chula Vista, CA (ARCO OTC). Sports Science staff have experienced increased requests for services, and additional staff positions at the LPOTC and ARCO Training Center make it possible for specific services to be provided. Resistance training programs are now available at all three locations by SS&T staff members who work cooperatively to provide consistency in the programs offered at the different OTCs. Physiological services are available by staff at LPOTC and are provided at the ARCO OTC by contract through a local university. Staff physiologists from any OTC may assist in providing services at the other locations. Engineering and computer technology services are primarily available from the CSOTC. Staff in these areas provide invaluable assistance particularly to SS&T biomechanists as well as develop and implement technology for coach/athlete use in their own specific areas of expertise. Biomechanical services are primarily provided by SS&T staff. In some instances, biomechanists associated with a local university may be asked to assist in providing services. These professionals may have unique instrumentation capabilities as well as sport specific interests and experience. Athletes at all three OTCs have access to biomechanical services as do athletes in certain training and/or competition venues at non-OTC sites. Regardless of the location for providing services, coaches working with athlete groups must interact with staff biomechanists so that specific questions related to athletic performance can be formulated. Sport-specific testing protocols can then be developed that will allow the biomechanists to begin answering the question(s) relevant to improving sport performance. Essential elements of providing quality biomechanical services include the following: (1) coach and sport federation (NGB) commitment and involvement, (2) information from testing sessions that is relevant, meaningful and sport-specific, (3) testing results that are returned in a reasonable timeframe, (4) serial testing of athletes, (5) focus on a limited number of variables initially, and (6) continuous evaluation and redesign of testing protocols as needed. To assist SS&T staff members in their efforts to develop and provide quality services to elite athletes in the United States, the Richard M. Scrushyl HealthSouth Sports Medicine and Sport Science Center was opened on the Olympic Complex in Colorado Springs in September, 1996. Within this new facility, a 3,000 sq. ft. Athlete Performance Laboratory provides space for both Biomechanics and Physiology testing of elite athletes. The Sports Biomechanics laboratory area houses a 4-camera ceiling-mounted video system and a centralized steel platform for mounting up to two Kistler or AMTI force plates. The camera system (zoom, aperature, focus, pan-andtilt) is operated from a control area with a viewing window that is adjacent to the laboratory. Within the main laboratory, seven additional camera locations can be used; an intercom system provides communication between the video system operator and those in the laboratory. Across the hall from the laboratory is the Biomechanics Analysis Laboratory. This area houses three motion measurements systems, a coach athlete review station, and a video editing system. Video tape storage cabinets are also located here. In addition to the new facility, equipment for data collection in the laboratory and field setting is also available. Three 120-Hz video systems; one 2001400-Hz video system; two AMTI force plates; eight Kistler force plates; 2D, 3D and pan-and-tile video systems; Data Logger for EMG and pressure measurements; instrumented pedals, luge handles, and punching bag; and computerized timing system are available for staff use. A video overlay system provides multi-functional capability for use with specific sports. PAST, PRESENT, AND FUTURE Quality biomechanical services are made possible by staff using existing facilities (laboratory andlor training/competition areas) and equipment appropriately. Using the 1996 Olympic Games as the Past and the 2002 Olympic Games as the Future, examples of how staff biomechanists have interacted and plans to continue or initiate involvement with various sports was presented

Extraction and Classification of Self-consumable Sport Video Highlights

This paper aims to automatically extract and classify self-consumable sport video highlights. For this purpose, we will emphasize the benefits of using play-break sequences as the effective inputs for HMM-based classifier. HMM is used to model the stochastic pattern of high-level states during specific sport highlights which correspond to the sequence of generic audio-visual measurements extracted from raw video data. This paper uses soccer as the domain study, focusing on the extraction and classification of goal, shot and foul highlights. The experiment work which uses183 play-break sequences from 6 soccer matches will be presented to demonstrate the performance of our proposed scheme

Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

Contextual cropping and scaling of TV productions

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows