Detecting Swimmers in Unconstrained Videos with Few Training Data-Reference-Cited by-同舟云学术

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Detecting Swimmers in Unconstrained Videos with Few Training Data

Published:2022 Issue: Volume: Page:145-154
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
Short-container-title:

Author:

Jacquelin Nicolas,Vuillemot Romain,Duffner Stefan

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-02044-5_12

Reference16 articles.

1. Benarab, D., Napoléon, T., Alfalou, A., Verney, A., Hellard, P.: A novel multitracking system for the evaluation of high-level swimmers performances. Baltimore, United States, May 2014

2. Benarab, D., Napoléon, T., Alfalou, A., Verney, A., Hellard, P.: Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques. Optics Commun. 356, 256–268 (2015)

3. Benarab, D., Napoléon, T., Alfalou, A., Verney, A., Hellard, P.: Optimized swimmer tracking system based on a novel multi-related-targets approach. Optics Lasers Eng. 89, 195–202 (2016)

4. Chapelle, O., Zien, A., Schölkopf, B.: Semi-Supervised Learning. MIT Press, Cambridge (2006)

5. COCO: Coco detection metric (2021). https://cocodataset.org/#detection-eval

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