Detection Accuracy of Soccer Players in Aerial Images Captured from Several Viewpoints
1
Department of Computer Science, Graduate School of Science and Technology, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki-shi 214-8517, Japan
2
Department of Computer Science, School of Science and Technology, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki-shi 214-8517, Japan
3
Department of Electrical and Electronic Engineering, Faculty of Engineering Science, Kansai University, 3-3-35 Yamate-cho, Suita-shi 564-8680, Japan
4
Graduate School of Engineering, Osaka City University, 3-3-138 Sugimoto Sumiyoshi-ku, Osaka-shi 558-8585, Japan
*
Author to whom correspondence should be addressed.
J. Funct. Morphol. Kinesiol. 2019, 4(1), 9; https://doi.org/10.3390/jfmk4010009
Received: 1 December 2018
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Revised: 7 January 2019
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Accepted: 11 January 2019
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Published: 21 January 2019
(This article belongs to the Special Issue Selected Papers from icSPORTS 2018)
In the fields of professional and amateur sports, players’ health, physical and physiological conditions during exercise should be properly monitored and managed. The authors of this paper previously proposed a real-time vital-sign monitoring system for players using a wireless multi-hop sensor network that transmits their vital data. However, existing routing schemes based on the received signal strength indicator or global positioning system do not work well, because of the high speeds and the density of sensor nodes attached to players. To solve this problem, we proposed a novel scheme, image-assisted routing (IAR), which estimates the locations of sensor nodes using images captured from cameras mounted on unmanned aerial vehicles. However, it is not clear where the best viewpoints are for aerial player detection. In this study, the authors investigated detection accuracy from several viewpoints using an aerial-image dataset generated with computer graphics. Experimental results show that the detection accuracy was best when the viewpoints were slightly distant from just above the center of the field. In the best case, the detection accuracy was very good: 0.005524 miss rate at 0.01 false positive-per-image. These results are informative for player detection using aerial images and can facilitate to realize IAR.
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Keywords:
player detection; aerial images; informed-filters
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MDPI and ACS Style
Oki, T.; Miyamoto, R.; Yomo, H.; Hara, S. Detection Accuracy of Soccer Players in Aerial Images Captured from Several Viewpoints. J. Funct. Morphol. Kinesiol. 2019, 4, 9. https://doi.org/10.3390/jfmk4010009
AMA Style
Oki T, Miyamoto R, Yomo H, Hara S. Detection Accuracy of Soccer Players in Aerial Images Captured from Several Viewpoints. Journal of Functional Morphology and Kinesiology. 2019; 4(1):9. https://doi.org/10.3390/jfmk4010009
Chicago/Turabian StyleOki, Takuro, Ryusuke Miyamoto, Hiroyuki Yomo, and Shinsuke Hara. 2019. "Detection Accuracy of Soccer Players in Aerial Images Captured from Several Viewpoints" Journal of Functional Morphology and Kinesiology 4, no. 1: 9. https://doi.org/10.3390/jfmk4010009
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