Robust trajectory estimation of soccer players by using two cameras

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

doi:10.1109/icpr.2008.4761154

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…They were able to achieve accurate, efficient scene detection using color distribution. Kasuya et al proposed a method to improve player tracking by using two images that are taken synchronously by two fixed cameras placed at a high angle [4]. However, they reported several problems in their method, including false detection caused when one player is detected as two regions, and defective detection ccaused by changes of sunlight and the low-resolution images.…”

Section: Related Workmentioning

confidence: 97%

A method for identification of moving objects by integrative use of a camera and accelerometers

Maruhashi

Terada

Tsukamoto

2012

Proceedings of the 27th Annual ACM Symposium on Applied Computing

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In sports events shown on TV or on the Internet, having the names and uniform numbers of the players displayed beside the players on the screen helps viewers to identify the players. Conventional methods of annotating and tracking objects utilize image recognition techniques with highresolution images, so players captured on camera from a long distance are too small for accurate recognition. In response to this problem, we propose a method for identifying and tracking moving objects with a combination of wearable acceleration sensors and image recognition techniques. In our method, the method recognizes moving objects by matching the context picked up by the wearable sensors with the context inferred during the image processing. Evaluation results demonstrated the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 97%

A method for identification of moving objects by integrative use of a camera and accelerometers

Maruhashi

Terada

Tsukamoto

2012

Proceedings of the 27th Annual ACM Symposium on Applied Computing

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“…We propose a method that estimates player trajectories under such conditions [9]. A key feature of our method is improving the estimation accuracy by utilizing player shadow regions generally considered a source of estimation error.…”

Section: Soccer Player Trackingmentioning

confidence: 99%

Real-time soccer player tracking method by utilizing shadow regions

Kasuya

Kitahara

Kameda

et al. 2010

Proceedings of the 18th ACM International Conference on Multimedia

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Our research aims to generate a player's view video stream by using a 3D free-viewpoint video technique. Since player trajectories are necessary to generate the video, we propose a realtime player trajectory estimation method by utilizing the shadow regions from soccer scenes. This paper describes our trial to realize real-time processing. We divide the process into capture and server computers. In addition, we reduced the processing cost with pipeline parallelization and optimization. We apply our proposed method to an actual soccer match held in a stadium and show its effectiveness.

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“…In a research on estimating the position of soccer players, Iwase et al 10) estimated the 3D position of a soccer player observed from multiple cameras. Kasuya et al 11) proposed a method to stably estimate the player's position in the soccer field by using a video captured by two cameras using shadow information. Because our research aimed to generate a Bullet-time video, the number of cameras arranged to surround the target object can be considerably greater.…”

Section: Related Workmentioning

confidence: 99%

[Papers] Method of Multiview Video Switching for Soccer Game Analysis in Large Scale Space

Shishido

Okada

Kameda

et al. 2020

MTA

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This paper proposes an automatic multiview video switching method for dynamic scenes for large-scale spaces. As shown in the upper part of Fig. 1, under our shooting conditions, the cameras are arranged more Abstract Research on tactical and performance analysis utilizes videos of dynamic sports scenes. An effective multiview video switching method can support the analysis. Bullet-time video is a multiview video browsing approach. Because the image is presented almost as it is, it is suitable for high-quality observations of the subject from multiple directions. This paper proposes a multiview image switching method for understanding dynamic scenes in large-scale spaces such as soccer games. We develop a prediction model for the camerawork for shooting Bullet-time videos. The model using deep neural network, which can estimate a suitable viewpoint to observe the target scene from the position information of the soccer players, ball, and goals.

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Robust trajectory estimation of soccer players by using two cameras

Cited by 8 publications

References 4 publications

A method for identification of moving objects by integrative use of a camera and accelerometers

A method for identification of moving objects by integrative use of a camera and accelerometers

Real-time soccer player tracking method by utilizing shadow regions

[Papers] Method of Multiview Video Switching for Soccer Game Analysis in Large Scale Space

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