Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16535
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dc.contributor.authorYadav, Puneet Kumar-
dc.contributor.authorTiwari, Shweta-
dc.contributor.authorJudeson Antony Kovilpillai, J-
dc.contributor.authorChinnaiyan, R-
dc.date.accessioned2024-08-29T05:41:25Z-
dc.date.available2024-08-29T05:41:25Z-
dc.date.issued2023-
dc.identifier.citationpp. 1-5en_US
dc.identifier.isbn9798350317060-
dc.identifier.urihttps://doi.org/10.1109/ICCAMS60113.2023.10526001-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16535-
dc.description.abstractFor both young and old, badminton is a sport. A badminton robot must be created for the real-time dynamic tracking of badminton travelling at high speed in order to swiftly and accurately capture the dynamic route of the game. We examine the impact of gravity and aerodynamic forces on badminton flight in the air in this essay. Experiments are used to analyze the key elements influencing the badminton shuttle's flight path. A steady-state flight model for badminton is created using the badminton flight trajectory recorded by a high-speed camera, along with a spin model. Considering that both model and measurement mistakes can occur during the badminton trajectory prediction process particle filtering is an inference model based on Monte Carlo sampling that is more suited to badminton state estimation since it does not require a priori knowledge of state transfer. Therefore, in this study, particle filtering is used to follow the badminton player's path. Following experimental validation, the algorithm is quite good at following the badminton ball's motion route. © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisher2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectAerodynamic Modelen_US
dc.subjectBadmintonen_US
dc.subjectComputer Visionen_US
dc.subjectConvolution Neural Network(Cnn)en_US
dc.subjectMachine Learningen_US
dc.titleAnalysis of Badminton Track Using Computer Vision Techniquesen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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