Real-time soccer player tracking method by utilizing shadow regions

Kasuya, Nozomu; Kitahara, Itaru; Kameda, Yoshinari; Ohta, Yuichi

doi:10.1145/1873951.1874211

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…11. There are many researches about sports player detection, recognition and tracking [16][17][18]. Players' positions and identities can be analyzed using these researches.…”

Section: Discussionmentioning

confidence: 99%

Camera Pose Estimation of a Smartphone at a Field without Interest Points

Miyano

Inoue

Minagawa

et al. 2013

Computer Vision - ACCV 2012 Workshops

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An Augmented Reality (AR) system on mobile phones has recently attracted attention because smartphones have increasingly been popular. For an AR system, we have to know a camera pose of a smartphone. A sensor-based method is one of the most popular ways to estimate the camera pose, but it cannot estimate an accurate pose. A vision-based method is another way to estimate the camera pose, but it is not suitable to a scene with few interest points such as a sports field. In this paper, we propose a novel method of a camera pose estimation for a scene without interest points by combining a sensor-based and a vision-based approach. In our proposed method, we use an acceleration and a magnetic sensor to roughly estimate a camera pose, then search the accurate pose by matching a captured image with a set of reference images. Our experiments show that our proposed method is accurate and fast enough to apply a real-time AR system.

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“…11. There are many researches about sports player detection, recognition and tracking [16][17][18]. Players' positions and identities can be analyzed using these researches.…”

Section: Discussionmentioning

confidence: 99%

Camera Pose Estimation of a Smartphone at a Field without Interest Points

Miyano

Inoue

Minagawa

et al. 2013

Computer Vision - ACCV 2012 Workshops

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“…In [23,24], an architecture was presented, which uses single (Figure 6a) and multi-cameras (Figure 6c) to capture a clear view of players and ball in various challenging and tricky situations such as severe occlusions and the ball being missing from the frames. To estimate the players' trajectory and team classification in [25,26] a bird's eye view of the field is presented to capture players, precisely as shown in Figure 6b. Various positions of the camera for capturing the entire field are presented in [27,28] to detect and track the players/ball and estimate the position of the players.…”

Section: Play Field Extraction In Various Sportsmentioning

confidence: 99%

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

2022

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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 such as soccer, basketball, cricket, and badminton, 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 players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and 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 available datasets related to a particular sport have been discussed. This paper reviews detailed discussion on some of the artificial intelligence (AI) applications, GPU-based work-stations and embedded platforms in sports vision. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

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“…A team of a player is estimated from a color of a uniform (Kasuya et al, 2010). And a player is identified from the team and a uniform number (Shitrit et al, 2011).…”

Section: Sports Player Analysismentioning

confidence: 99%

A Mobile AR System for Sports Spectators using Multiple Viewpoint Cameras

Miyano

Inoue

Minagawa

et al. 2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:In this paper, we aim to develop an AR system which supports spectators who are watching a sports game using smartphones in a spectators' stand. The final goal of this system is that a spectator can watch information of players through a smartphone and share experiences with other spectators. For this goal, we propose a system which consists of smartphones and fixed cameras. Fixed cameras are set to cover the whole sports field and used to analyze players. Smartphones held by spectators are used to estimate positions where they are looking on the sports field. We built an AR system which makes annotation of players' information onto a smartphone image. And we evaluated the accuracy and the processing time of our system and revealed its practicality.

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Real-time soccer player tracking method by utilizing shadow regions

Cited by 6 publications

References 8 publications

Camera Pose Estimation of a Smartphone at a Field without Interest Points

Camera Pose Estimation of a Smartphone at a Field without Interest Points

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

A Mobile AR System for Sports Spectators using Multiple Viewpoint Cameras

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