A study on table tennis landing point detection algorithm based on spatial domain information
Abstract To address the limitations of computer vision-assisted table tennis ball detection, which heavily relies on vision acquisition equipment and exhibits slow processing speed, we propose a real-time calculation method for determining the landing point of table tennis balls. This novel approach...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42966-6 |
| _version_ | 1827634942726635520 |
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| author | Tao Ning Changcheng Wang Meng Fu Xiaodong Duan |
| author_facet | Tao Ning Changcheng Wang Meng Fu Xiaodong Duan |
| author_sort | Tao Ning |
| collection | DOAJ |
| description | Abstract To address the limitations of computer vision-assisted table tennis ball detection, which heavily relies on vision acquisition equipment and exhibits slow processing speed, we propose a real-time calculation method for determining the landing point of table tennis balls. This novel approach is based on spatial domain information and reduces the dependency on vision acquisition equipment. This method incorporates several steps: employing dynamic color thresholding to determine the centroid coordinates of all objects in the video frames, utilizing target area thresholding and spatial Euclidean distance to eliminate interference balls and noise, optimizing the total number of video frames through keyframe extraction to reduce the number of operations for object recognition and landing point detection, and employing the four-frame difference slope method and polygonal area determination to detect the landing point and area of the target object, thereby obtaining precise coordinates and their corresponding areas. Experimental results on the above method on the Jetson Nano development board show that the dynamic color thresholding method achieves a detection speed of 45.3 fps. The keyframe extraction method correctly identifies the landing point frames with an accuracy rate exceeding 93.3%. In terms of drop point detection, the proposed method achieves 78.5% overall accuracy in detecting table tennis ball drop points while ensuring real-time detection. These experiments validate that the proposed method has the ability to detect table tennis ball drop points in real time and accurately in low frame rate vision acquisition devices and real environments. |
| first_indexed | 2024-03-09T15:21:19Z |
| format | Article |
| id | doaj.art-46380be554b0429a94fb9b7a66713c61 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:21:19Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-46380be554b0429a94fb9b7a66713c612023-11-26T12:48:37ZengNature PortfolioScientific Reports2045-23222023-11-0113111210.1038/s41598-023-42966-6A study on table tennis landing point detection algorithm based on spatial domain informationTao Ning0Changcheng Wang1Meng Fu2Xiaodong Duan3State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology, Institute of Computer Science, Dalian Minzu UniversityState Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology, Institute of Computer Science, Dalian Minzu UniversityState Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology, Institute of Computer Science, Dalian Minzu UniversityState Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology, Institute of Computer Science, Dalian Minzu UniversityAbstract To address the limitations of computer vision-assisted table tennis ball detection, which heavily relies on vision acquisition equipment and exhibits slow processing speed, we propose a real-time calculation method for determining the landing point of table tennis balls. This novel approach is based on spatial domain information and reduces the dependency on vision acquisition equipment. This method incorporates several steps: employing dynamic color thresholding to determine the centroid coordinates of all objects in the video frames, utilizing target area thresholding and spatial Euclidean distance to eliminate interference balls and noise, optimizing the total number of video frames through keyframe extraction to reduce the number of operations for object recognition and landing point detection, and employing the four-frame difference slope method and polygonal area determination to detect the landing point and area of the target object, thereby obtaining precise coordinates and their corresponding areas. Experimental results on the above method on the Jetson Nano development board show that the dynamic color thresholding method achieves a detection speed of 45.3 fps. The keyframe extraction method correctly identifies the landing point frames with an accuracy rate exceeding 93.3%. In terms of drop point detection, the proposed method achieves 78.5% overall accuracy in detecting table tennis ball drop points while ensuring real-time detection. These experiments validate that the proposed method has the ability to detect table tennis ball drop points in real time and accurately in low frame rate vision acquisition devices and real environments.https://doi.org/10.1038/s41598-023-42966-6 |
| spellingShingle | Tao Ning Changcheng Wang Meng Fu Xiaodong Duan A study on table tennis landing point detection algorithm based on spatial domain information Scientific Reports |
| title | A study on table tennis landing point detection algorithm based on spatial domain information |
| title_full | A study on table tennis landing point detection algorithm based on spatial domain information |
| title_fullStr | A study on table tennis landing point detection algorithm based on spatial domain information |
| title_full_unstemmed | A study on table tennis landing point detection algorithm based on spatial domain information |
| title_short | A study on table tennis landing point detection algorithm based on spatial domain information |
| title_sort | study on table tennis landing point detection algorithm based on spatial domain information |
| url | https://doi.org/10.1038/s41598-023-42966-6 |
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