Skip to Main content Skip to Navigation
Conference papers

Detecting Swimmers in Unconstrained Videos with Few Training Data

Nicolas Jacquelin 1, 2, 3 Stefan Duffner 1, 2 Romain Vuillemot 1, 3
2 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 SICAL - Situated Interaction, Collaboration, Adaptation and Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this work, we propose a method to detect swimmers in unconstrained swimming video, using a Unet-based model trained on a small dataset. Our main motivation is to make the method accessible without spending much time or money in annotation or computation while maintaining performance. As a result, our model reaches topperformances in detection without requiring for intrusive sensors. The swimming videos can be recorded from various locations with different settings (distance and angle to the pool, light conditions, reflections, camera resolution), which alleviates a lot of the usual video capture constraints. Every algorithm described in the paper is accessible online at https://github.com/njacquelin/swimmers detection.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03358375
Contributor : Nicolas Jacquelin Connect in order to contact the contributor
Submitted on : Wednesday, September 29, 2021 - 12:38:38 PM
Last modification on : Monday, October 11, 2021 - 7:08:02 PM

File

Detecting Swimmers in Unconstr...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03358375, version 1

Citation

Nicolas Jacquelin, Stefan Duffner, Romain Vuillemot. Detecting Swimmers in Unconstrained Videos with Few Training Data. Machine Learning and Data Mining for Sports Analytics, Sep 2021, Ghand, Belgium. ⟨hal-03358375⟩

Share

Metrics

Record views

37

Files downloads

80