Semi-automatic tracking of beach volleyball players (original) (raw)

Tracking of Ball and Players in Beach Volleyball Videos

PLoS ONE, 2014

This paper presents methods for the determination of players' positions and contact time points by tracking the players and the ball in beach volleyball videos. Two player tracking methods are compared, a classical particle filter and a rigid grid integral histogram tracker. Due to mutual occlusion of the players and the camera perspective, results are best for the front players, with 74,6% and 82,6% of correctly tracked frames for the particle method and the integral histogram method, respectively. Results suggest an improved robustness against player confusion between different particle sets when tracking with a rigid grid approach. Faster processing and less player confusions make this method superior to the classical particle filter. Two different ball tracking methods are used that detect ball candidates from movement difference images using a background subtraction algorithm. Ball trajectories are estimated and interpolated from parabolic flight equations. The tracking accuracy of the ball is 54,2% for the trajectory growth method and 42,1% for the Hough line detection method. Tracking results of over 90% from the literature could not be confirmed. Ball contact frames were estimated from parabolic trajectory intersection, resulting in 48,9% of correctly estimated ball contact points.

Visual tracking for sports applications

2005

Visual tracking of the human body has attracted increasing attention due to the potential to perform high volume low cost analyses of motions in a wide range of applications, including sports training, rehabilitation and security. In this paper we present the development of a visual tracking module for a system aimed to be used as an autonomous instructional aid for amateur golfers. Postural information is captured visually and fused with information from a golf swing analyser mat and both visual and audio feedback given based on the golfers mistakes. Results from the visual tracking module are presented.

Automatic Tracking of Indoor Soccer Players Using Videos from Multiple Cameras

2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, 2012

Indoor soccer has been of tactical and scientific interest, with applications dedicated to analyze tactical and physiological factors and also physical training. In both cases, the analysis is based on player tracking, done with human supervision. This paper presents an automatic tracking method which shows the trajectories of indoor soccer players during the game and saving skilled labor during the process. For this, we use a predictive filter to model the motion and the observation of multiple stationary cameras, strategically positioned around the court. We associate a particle filter to a robust probabilistic observation model with the measurement in court coordinates. The observation model proposed is based on data fusion across multiple camera coordinates and projected onto the court plane, creating a multimodal and bidirectional probability function, which represents the potential localization of players in the court plane. The probability function uses an appearance model to observe player's location, distinguishing very close players and yielding good weights in the observation model. The experimental results show tracking errors below 70 centimeters in most cases and indicate the potential of the method to help sports teams.

A vision-based system to support tactical and physical analyses in futsal

Machine Vision and Applications, 2017

This paper presents a vision-based system to support tactical and physical analyses of futsal teams. Most part of the current analyses in this sport are manually performed, while the existing solutions based on automatic approaches are frequently composed by costly and complex tools, developed for other kind of team sports, making it difficult their adoption by futsal teams. Our system, on the other hand, represents a simple yet efficient dedicated solution, which is based on the analyses of image sequences captured by a single stationary camera used to obtain top-view images of the entire court. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to track them in every video frame. The system determines the distance traveled by each player, his/her mean and maximum speeds, as well as generates heat maps that describe players' occupancy during the match. To present the collected data, our system uses a specially developed mobile application. Experimental results with image sequences of an official match and a training match show that our system provides data with global mean tracking errors below 40 cm, demanding on 25 ms to process each frame and, thus, demonstrating its high application potential.

A modular system for tracking players in sports games

International Journal of Education, 2009

Detection, recognition and tracking of the players in sports games which is based on image processing and computer vision is the topic of interest of various research groups. Different approaches regarding choice and placement of the acquisition hardware exist. Regardless of whether a custom set of static cameras is used or the images are acquired from directed TV coverage, player detection and tracking is a complex process. Robustness of such a system is the main goal of its designers. In this paper, a modular system for tracking indoor and outdoor team games using computer vision is proposed and described. Proposed approach is flexible and expandable so it can be said that overall system framework provides robustness of detection, recognition and tracking.

Automated Sports Player Performance Analysis: A Survey

النشرة المعلوماتیة فی الحاسبات والمعلومات, 2021

Sports can not be said it is a mean for social cohesion by allowing people interaction disregarding their social status, age, etc. only but also it became the entertainment industry from governmental views. Consequently, a large amount of resources has been directed to sports to improve understanding, performance and presentation. Sports video analysis is a popular tool to capture low-level and high-level analysis. Sports analytics have several usage scenarios such as personalized video summary, player statistics and tactical analysis. In order to make the automatic sports video analysis realized, there is a necessity for player identification across sports. Identifying players in sports videos is a major research challenge due to low video resolution per player, camera perspective, the variation of the posture of player, illumination conditions, variety of sports fields and t-shirts. Here, we survey the most significant methods and problem formulations that have been proposed to ad...

Vision system for tracking handball players using fuzzy color processing

Machine Vision and Applications, 2013

The sports community needs technological aid to extract accurate statistics and performance data from both practice sessions and games. To obtain such information, players must be tracked over time and their movements processed so that individual actions and team plays are simultaneously analyzed. In order to perform this analysis in an automated, formal and accurate way, the authors developed a cost conscientious processing system fed by two overhead cameras (roughly one video stream for each half-field). Players are detected by vest colors, and Fuzzy Logic is used to allow for a given color to be shared by different teams. Color models for the background and the teams are dynamic over time to make up for changes in natural lighting conditions and consequent color changes. Player tracking is further enhanced using Kalman Filtering. Some examples of the analysis, made possible by the proposed system, are shown. Results are based on videos collected during the Portuguese Handball SuperCup competition for the year 2011.

Sentioscope: A Soccer Player Tracking System Using Model Field Particles

IEEE Transactions on Circuits and Systems for Video Technology, 2016

Tracking multiple players is crucial to analyze soccer videos in real time. Yet, rapid illumination changes and occlusions among players who look similar from a distance make tracking in soccer very difficult. Particle-filter-based approaches have been utilized for their ability in tracking under occlusion and rapid motions. Unlike the common practice of choosing particles on targets, we introduce the notion of shared particles densely sampled at fixed positions on the model field. We globally evaluate targets' likelihood of being on the model field particles using our combined appearance and motion model. This allows us to encapsulate the interactions among the targets in the statespace model and track players through challenging occlusions. The proposed tracking algorithm is embedded into a real-life soccer player tracking system called Sentioscope. We describe the complete steps of the system and evaluate our approach on large-scale video data gathered from professional soccer league matches. The experimental results show that the proposed algorithm is more successful, compared with the previous methods, in multiple-object tracking with similar appearances and unpredictable motion patterns such as in team sports. Index Terms-Model field particles, multiple-object tracking, Sentioscope, soccer player tracking, sports video analysis. I. INTRODUCTION S OCCER (football) is among the world's most popular sports played by millions of people around the world. Such popularity has led many computer vision researchers to work on soccer video analysis. A wide spectrum of such applications has been introduced to offer team/player performance analysis, referee decision support, video summarization, highlight extraction, and intelligent broadcast cameras [1]. Team/player performance measurement systems has the potential to reveal aspects of the game that are not obvious to the human eye. Such systems can measure the distance covered by players, speed of movement, number of sprints, and players' relative positioning with respect to others. This data are then used in individual player performance evaluation, fatigue detection, assessment of team's tactical performance and analysis of the opponents. Accurate tracking of multiple soccer players in real time is the key issue in performance evaluation, and requires detecting Manuscript

Errors and mistakes in automated player tracking

B. Likar (ed.), Proceedings of the Sixth …, 2001

This paper examines errors, which can affect the accuracy of computer vision based people tracker. After a successful development of an automated player tracking system for use in team sports, the set of experiments was designed to investigate its accuracy. The authors take advantage of the controlled environment they use to obtain the "ground truth" information. This information is used to measure the accuracy of the tracking system. The results obtained are analyzed, and conclusions, including specification of the overall tracker accuracy, are given.