An All Deep System for Badminton Game Analysis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914678237560832 |
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| author | Chou, Po-Yung Lo, Yu-Chun Xie, Bo-Zheng Lin, Cheng-Hung Kao, Yu-Yung |
| author_facet | Chou, Po-Yung Lo, Yu-Chun Xie, Bo-Zheng Lin, Cheng-Hung Kao, Yu-Yung |
| contents | The CoachAI Badminton 2023 Track1 initiative aim to automatically detect events within badminton match videos. Detecting small objects, especially the shuttlecock, is of quite importance and demands high precision within the challenge. Such detection is crucial for tasks like hit count, hitting time, and hitting location. However, even after revising the well-regarded shuttlecock detecting model, TrackNet, our object detection models still fall short of the desired accuracy. To address this issue, we've implemented various deep learning methods to tackle the problems arising from noisy detectied data, leveraging diverse data types to improve precision. In this report, we detail the detection model modifications we've made and our approach to the 11 tasks. Notably, our system garnered a score of 0.78 out of 1.0 in the challenge. We have released our source code in Github https://github.com/jean50621/Badminton_Challenge |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_12645 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An All Deep System for Badminton Game Analysis Chou, Po-Yung Lo, Yu-Chun Xie, Bo-Zheng Lin, Cheng-Hung Kao, Yu-Yung Computer Vision and Pattern Recognition The CoachAI Badminton 2023 Track1 initiative aim to automatically detect events within badminton match videos. Detecting small objects, especially the shuttlecock, is of quite importance and demands high precision within the challenge. Such detection is crucial for tasks like hit count, hitting time, and hitting location. However, even after revising the well-regarded shuttlecock detecting model, TrackNet, our object detection models still fall short of the desired accuracy. To address this issue, we've implemented various deep learning methods to tackle the problems arising from noisy detectied data, leveraging diverse data types to improve precision. In this report, we detail the detection model modifications we've made and our approach to the 11 tasks. Notably, our system garnered a score of 0.78 out of 1.0 in the challenge. We have released our source code in Github https://github.com/jean50621/Badminton_Challenge |
| title | An All Deep System for Badminton Game Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.12645 |