| Paper Abstract and Keywords |
| Presentation |
2023-02-21 15:30
Evaluating The Effectiveness of Data Augmentation for Learning TrackNetV2
Yushan Wang (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Data augmentation has been widely used in a variety of deep learning tasks, mostly with a positive impact on the results. Nevertheless, for the shuttlecock detection task, data augmentation has not been applied to state-of-the-art (SOTA) algorithms. In this work we apply data augmentation to a state-of-the-art shuttlecock detection algorithm, TrackNetV2, to evaluate its effectiveness. Data augmentation has usually been applied to a single frame, but in our problem, consecutive frames extracted from a video need to be an input. Therefore, in this work we consider multi-frame data augmentation: Specifically, we apply the same transformation with the same parameters to consecutive frames being fed at the same time in online manner. Experimental results on a public shuttlecock detection dataset demonstrates the effectiveness of our approach: We got improvements with respect to the precision, recall, F1 score and Accuracy from 84.52%, 81.65%, 83.06%, 75.48% to 86.85%, 81.78%, 84.24%, 77.01%. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Data augmentation / deep learning / shuttlecock detection / / / / / |
| Reference Info. |
ITE Tech. Rep., vol. 47, no. 6, ME2023-36, pp. 81-84, Feb. 2023. |
| Paper # |
ME2023-36 |
| Date of Issue |
2023-02-14 (MMS, ME, AIT) |
| ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
| Download PDF |
|
| Conference Information |
| Committee |
MMS ME AIT IEICE-IE IEICE-ITS |
| 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) |
Evaluating The Effectiveness of Data Augmentation for Learning TrackNetV2 |
| Sub Title (in English) |
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| Keyword(1) |
Data augmentation |
| Keyword(2) |
deep learning |
| Keyword(3) |
shuttlecock detection |
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| 1st Author's Name |
Yushan Wang |
| 1st Author's Affiliation |
Tokyo Metropolitan University (TMU) |
| 2nd Author's Name |
Shuhei Tarashima |
| 2nd Author's Affiliation |
NTT Communications Corporation (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 15:30:00 |
| Presentation Time |
15 minutes |
| Registration for |
ME |
| Paper # |
MMS2023-16, ME2023-36, AIT2023-16 |
| Volume (vol) |
vol.47 |
| Number (no) |
no.6 |
| Page |
pp.81-84 |
| #Pages |
4 |
| Date of Issue |
2023-02-14 (MMS, ME, AIT) |