Widely Applicable Strong Baseline for Sports Ball Detection and Tracking
Shuhei Tarashima (NTT Communications / Tokyo Metropolitan University),* Muhammad Abdul Haq (Tokyo Metropolitan University), Yushan Wang (Tokyo Metropolitan University), Norio Tagawa (Tokyo Metropolitan University)
The 34th British Machine Vision Conference
Abstract
In this work, we present a novel Sports Ball Detection and Tracking (SBDT) method that can be applied to various sports categories. Our approach is composed of (1) high-resolution feature extraction, (2) position-aware model training, and (3) inference considering temporal consistency, all of which are put together as a new SBDT baseline. Besides, to validate the wide-applicability of our approach, we compare our baseline with 6 state-of-the-art SBDT methods on 5 datasets from different sports categories. We achieve this by newly introducing two SBDT datasets, providing new ball annotations for two datasets, and re-implementing all the methods to ease extensive comparison. Experimental results demonstrate that our approach is substantially superior to existing methods on all the sports categories covered by the datasets. We believe our proposed method can play as a Widely Applicable Strong Baseline (WASB) of SBDT, and our datasets and codebase will promote future SBDT research. Datasets and codes are available at https://github.com/nttcom/WASB-SBDT.Video
Citation
@inproceedings{Tarashima_2023_BMVC,
author = {Shuhei Tarashima and Muhammad Abdul Haq and Yushan Wang and Norio Tagawa},
title = {Widely Applicable Strong Baseline for Sports Ball Detection and Tracking},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0310.pdf}
}
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