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Fully automatic camera for personalized highlight generation in sporting events

Robbe Decorte (UGent) , Jelle De Bock (UGent) , Joachim Taelman (UGent) , Maarten Slembrouck (UGent) and Steven Verstockt (UGent)
(2024) SENSORS. 24(3).
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Abstract
Personally curated content in short-form video formats provides added value for participants and spectators but is often disregarded in lower-level events because it is too labor-intensive to create or is not recorded at all. Our smart sensor-driven tripod focuses on supplying a unified sensor and video solution to capture personalized highlights for participants in various sporting events with low computational and hardware costs. The relevant parts of the video for each participant are automatically determined by using the timestamps of his/her received sensor data. This is achieved through a customizable clipping mechanism that processes and optimizes both video and sensor data. The clipping mechanism is driven by sensing nearby signals of Adaptive Network Topology (ANT+) capable devices worn by the athletes that provide both locality information and identification. The device was deployed and tested in an amateur-level cycling race in which it provided clips with a detection rate of 92.9%. The associated sensor data were used to automatically extract peloton passages and report riders' positions on the course, as well as which participants were grouped together. Insights derived from sensor signals can be processed and published in real time, and an upload optimization scheme is proposed that can provide video clips for each rider a maximum of 5 min after the passage if video upload is enabled.
Keywords
personalized clips, highlight generation, sensor tracking, video enrichment, sports data science, SYSTEM, VIDEO

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Citation

Please use this url to cite or link to this publication:

MLA
Decorte, Robbe, et al. “Fully Automatic Camera for Personalized Highlight Generation in Sporting Events.” SENSORS, vol. 24, no. 3, 2024, doi:10.3390/s24030736.
APA
Decorte, R., De Bock, J., Taelman, J., Slembrouck, M., & Verstockt, S. (2024). Fully automatic camera for personalized highlight generation in sporting events. SENSORS, 24(3). https://doi.org/10.3390/s24030736
Chicago author-date
Decorte, Robbe, Jelle De Bock, Joachim Taelman, Maarten Slembrouck, and Steven Verstockt. 2024. “Fully Automatic Camera for Personalized Highlight Generation in Sporting Events.” SENSORS 24 (3). https://doi.org/10.3390/s24030736.
Chicago author-date (all authors)
Decorte, Robbe, Jelle De Bock, Joachim Taelman, Maarten Slembrouck, and Steven Verstockt. 2024. “Fully Automatic Camera for Personalized Highlight Generation in Sporting Events.” SENSORS 24 (3). doi:10.3390/s24030736.
Vancouver
1.
Decorte R, De Bock J, Taelman J, Slembrouck M, Verstockt S. Fully automatic camera for personalized highlight generation in sporting events. SENSORS. 2024;24(3).
IEEE
[1]
R. Decorte, J. De Bock, J. Taelman, M. Slembrouck, and S. Verstockt, “Fully automatic camera for personalized highlight generation in sporting events,” SENSORS, vol. 24, no. 3, 2024.
@article{01HQ5EJJT0AXCM58DXG0M8GGHH,
  abstract     = {{Personally curated content in short-form video formats provides added value for participants and spectators but is often disregarded in lower-level events because it is too labor-intensive to create or is not recorded at all. Our smart sensor-driven tripod focuses on supplying a unified sensor and video solution to capture personalized highlights for participants in various sporting events with low computational and hardware costs. The relevant parts of the video for each participant are automatically determined by using the timestamps of his/her received sensor data. This is achieved through a customizable clipping mechanism that processes and optimizes both video and sensor data. The clipping mechanism is driven by sensing nearby signals of Adaptive Network Topology (ANT+) capable devices worn by the athletes that provide both locality information and identification. The device was deployed and tested in an amateur-level cycling race in which it provided clips with a detection rate of 92.9%. The associated sensor data were used to automatically extract peloton passages and report riders' positions on the course, as well as which participants were grouped together. Insights derived from sensor signals can be processed and published in real time, and an upload optimization scheme is proposed that can provide video clips for each rider a maximum of 5 min after the passage if video upload is enabled.}},
  articleno    = {{736}},
  author       = {{Decorte, Robbe and De Bock, Jelle and Taelman, Joachim and Slembrouck, Maarten and Verstockt, Steven}},
  issn         = {{1424-8220}},
  journal      = {{SENSORS}},
  keywords     = {{personalized clips,highlight generation,sensor tracking,video enrichment,sports data science,SYSTEM,VIDEO}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{21}},
  title        = {{Fully automatic camera for personalized highlight generation in sporting events}},
  url          = {{http://doi.org/10.3390/s24030736}},
  volume       = {{24}},
  year         = {{2024}},
}

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