Semi-automatic tracking of beach volleyball players (original) (raw)
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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.
ArXiv, 2022
Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting the trajectories of player or ball, recognizing the team‟s strategies, classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher‟s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly avail...
Probabilistic tracking of soccer players and ball
Proc. Asian Conf. Computer Vision, 2004
An effective system simultaneously tracking multiple players and a ball on broadcasted soccer matches is proposed in this paper. This system uses particle filter with synthesized images from templates for tracking players of the same team in occlusion. This synthesized image where an adaptive color histogram is made from means an expected image for each particle and gives more precise likelihood evaluation of the particles. For ball tracking, when the ball is in ballistic motion without any interruption of players, an ordinary particle filter estimates the state of the ball. When the ball is considered to be possessed by a player or players, the tracker stops, waits for the ball to reappear in the area around the corresponding players. This tracker gives good performance on the commonly broadcasted soccer match videos.
Multiple interacting targets tracking with application to team sports
2005
The interest in the field of computer aided analysis of sport events is ever growing and the ability of tracking objects during a sport event has become an elementary task for nearly every sport analysis system. We present in this paper a color based probabilistic tracker that is suitable for tracking players on the playground during a sport game. Since the players are being tracked in their natural environment, and this environment is subjected to certain rules of the game, we use the concept of closed worlds, to model the scene context and thus improve the reliability of tracking. * The paper appeared in the proceedings of the 4th International Symposium on Image and Signal Processing and Analysis ISPA,September 2005, pp.322-327. tions by a new assumption, and extends the tracker to the case of multiple players. Section 9 describes the experiments for evaluation of the tracker, and in Section 10 we draw some conclusions.
Tracking Players in Indoor Sports Using a Vision System Inspired in Fuzzy and Parallel Processing
Sports are an important part of nowadays society and there is an increasing interest by the sports' community on having mechanisms that allow them to better understand the dynamics of teams (their own and their opponents). This information is frequently extracted manually by operators that, after the game, visualize game recordings (frequently TV footages) and perform hand annotation, which is a time consuming and error prone task.
Low Cost Player Tracking in Field Hockey
Communications in computer and information science, 2022
In the paper, we describe the technical details of a multiplayer tracker system using tracking data obtained from a single low-cost stationary camera on field hockey games. Analyzing the tracking data of the players only from the transmitted video opens a multitude of applications that allows the cost of technology to be reduced. This method does not depend on the cooperation of the players (by using sensors) or their teams (by sharing data with a third party). The approach taken in this paper uses a variety of computer vision and tracking techniques. Making player tracking data more accessible lowers the barrier to entry for sports research and increases the period during which advanced analysis methods can be applied. The proposed system runs the full pipeline at 3 fps on a computer with a simple graphics card.
Particle Filter-Based Predictive Tracking of Futsal Players from a Single Stationary Camera
2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, 2015
In this paper we study the use of computer vision techniques for visual tracking of futsal players. In the sports field, player tracking is an important task, as it can provide an estimate of the position of the athlete in a given time and thus compute his/her trajectories. This information can be used by coaches and sport professionals on tactical and physical analyses. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to predict their positions and track them using data from a single stationary camera. Experimental results show that our approach is capable to track players and compute their trajectories over time with errors below 20 cm, thus demonstrating a high potential to be used in a wide range of futsal match analyses.