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. 2022 Jan 26;22(3):955.
doi: 10.3390/s22030955.

Vision-Based System for Automated Estimation of the Frontal Area of Swimmers: Towards the Determination of the Instant Active Drag: A Pilot Study

José M González-Ravé  1 , Francisco Moya-Fernández  2 , Francisco Hermosilla-Perona  1   3   4 , Fernando J Castillo-García  2
Affiliations

Vision-Based System for Automated Estimation of the Frontal Area of Swimmers: Towards the Determination of the Instant Active Drag: A Pilot Study

José M González-Ravé et al. Sensors (Basel). .

Abstract

Swimmers take great advantage by reducing the drag forces either in passive or active conditions. The purpose of this work is to determine the frontal area of swimmers by means of an automated vision system. The proposed algorithm is automated and also allows to determine lateral pose of the swimmer for training purposes. In this way, a step towards the determination of the instantaneous active drag is reached that could be obtained by correlating the effective frontal area of the swimmer to the velocity. This article shows a novel algorithm for estimating the frontal and lateral area in comparison with other models. The computing time allows to obtain a reasonable online representation of the results. The development of an automated method to obtain the frontal surface area during swimming increases the knowledge of the temporal fluctuation of the frontal surface area in swimming. It would allow the best monitoring of a swimmer in their swimming training sessions. Further works will present the complete device, which allows to track the swimmer while acquiring the images and a more realistic model of conventional active drag ones.

Keywords: active drag; area determination; computer vision; resistive force; swimming.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Pictures of the swimmer in different poses.
Figure 2
Figure 2
(a) Scenario description; (b) Cameras position.
Figure 3
Figure 3
Example of references used during the cameras calibration.
Figure 4
Figure 4
Examples of videoframes obtained by frontal camera (a,b) and lateral one (c,d).
Figure 5
Figure 5
Summary of the algorithm steps for swimmer area determination. (a) Data Flow Diagram; (b) Pseudocode.
Figure 6
Figure 6
Region of interest (ROI) of the frontal camera.
Figure 7
Figure 7
Example of application of the mask to substitute swimwear by skin colour (a,b) showing pre- and post-processing image, respectively.
Figure 8
Figure 8
Examples of application of the mask to remove water and elements up to water line.
Figure 9
Figure 9
(a) Original frame; (b) grayscale conversion; (c) binarisation; (d) area filter.
Figure 10
Figure 10
Frontal area estimation of several frames.
Figure 11
Figure 11
Example of computing time of the frontal area estimation algorithm.
Figure 12
Figure 12
Lateral area estimation of several frames.
Figure 13
Figure 13
Time evolution of the frontal and lateral area. Test I.
Figure 14
Figure 14
Time evolution of the frontal and lateral area. Test II.
Figure 15
Figure 15
Time evolution of the frontal and lateral area: (a) Test I; (b) Test II.
Figure 16
Figure 16
Frequency spectrum of Frontal and Lateral Area for Test I.
Figure 17
Figure 17
Conceptual device for correlating frontal area to velocity.
See this image and copyright information in PMC

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