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Paper Abstract and Keywords
Presentation 2023-02-21 15:45
A Residual U-Net Architecture for Shuttlecock Detection
Muhammad Abdul Haq (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU)
Abstract (in Japanese) (See Japanese page) 
(in English) Detection of fast-moving shuttlecocks is essential for badminton video analysis. Several methods based on deep learning have been proposed in the literature. TrackNetV2 is a state-of-the-art shuttlecock detection model which uses the U-Net architecture, but we believe there is room for further performance improvements. In this research, we extend TrackNetV2 via employing deep residual learning. Experimental results on a public shuttlecock detection dataset demonstrates that our proposed method performs better than the original TrackNetV2 with respect to precision, recall, F1 score and accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Shuttlecock detection / Deep learning / Residual network / / / / /  
Reference Info. ITE Tech. Rep., vol. 47, no. 6, ME2023-37, pp. 85-88, Feb. 2023.
Paper # ME2023-37 
Date of Issue 2023-02-14 (MMS, ME, AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Conference Date 2023-02-21 - 2023-02-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ME 
Conference Code 2023-02-MMS-ME-AIT-IE-ITS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Residual U-Net Architecture for Shuttlecock Detection 
Sub Title (in English)  
Keyword(1) Shuttlecock detection  
Keyword(2) Deep learning  
Keyword(3) Residual network  
1st Author's Name Muhammad Abdul Haq  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
2nd Author's Name Shuhei Tarashima  
2nd Author's Affiliation NTT Communications (NTT Com)
3rd Author's Name Norio Tagawa  
3rd Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2023-02-21 15:45:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # MMS2023-17, ME2023-37, AIT2023-17 
Volume (vol) vol.47 
Number (no) no.6 
Page pp.85-88 
Date of Issue 2023-02-14 (MMS, ME, AIT) 

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