# Preliminary results of YOLO-Based Recognition of Surface Markers in Aquatic Environments for Equine Motion Analysis Applications

Frédéric Marin*\*a, Chloé Giraudet\*a, Claire Moiroud\*b, Pauline Gaulmin\*b, Audrey Beaumont\*b, Emeline De Azevedo\*b, Fabrice Audigié\*b, Henry Chateau\*b

a Université de technologie de Compiègne, France

b Ecole Nationale Vétérinaire d'Alfort, France

* Corresponding author: frederic.marin@utc.fr

Received date: 04/04/2024

Accepted date: 28/06/2024

Publication date: 31/01/2025

**Keywords:** motion capture, horse, swim, deep learning

© 2025 The Authors
Licence CC-BY 4.0
Published by Société de Biomécanique

## 1. Introduction

Although markerless motion capture systems are currently under development and promising, the technique involving landmarkers remains the gold standard either for obtaining labeled data for motion capture reference databases or conducting motion analysis in a conventional manner (Chaumeil et. 2024).

Although marker recognition techniques using retro-reflective spherical markers are well-established (Robertson et al. 2014), they prove impractical in aquatic environments. Consequently, the use of surface markers in natural light is preferred. Specifically, in the study of horse swimming for rehabilitation or muscle reinforcement, the use of color-differentiated markers corresponding to the horse's color and variations in light, which create shadows influenced by the animal's size in the swimming lane, pose challenges for conventional marker detection techniques (Giraudet, Moiroud, et al., 2023). The purpose of this study was to assess the feasibility and reliability of using the deep convolutional neural networks (CNN) model of the YOLO-v2 type which allows the detection of objects in an image for the tracking of surface markers of various colors in the aquatic environment on the horse.

## 2. Methods

One horse was recorded swimming in the U-shaped pool at the Equine Clinic (CIRALE) of the National Veterinary College of Alfort, using six GoPro cameras (GoPro Hero 8 Black, GoPro, San Mateo, USA)

with a resolution of 2.7K and a frame rate of 120 Hz. Anatomical landmarks on the horse were identified using surface markers applied with animal marking sticks (Raidex, Dettingen, Germany) (Giraudet, Moiroud, et al., 2023). This study was approved by the clinical research and ethics committee of the Alfort Veterinary School (protocol code: 2022-09-19). Each video underwent specific post-processing to correct distortions related to aquatic environments, and the surface markers were identified semi-automatically with manual corrections (Giraudet et al. 2023). A total of 2780 images with 17616 surface markers labeled were used. Each frame of the video was divided into 96 sub-images of a resolution of 190x208 px. Subsequently, a CNN YOLO v2 model based on pre-trained features extraction resnet-50 was trained (Redmon et al. 2016). A total of 29302 sub-images were used as training data set, 4884 as validation dataset and 14651 as test dataset. The training algorithm utilized the “adam” optimizer and a mini batch size of 20 over 5 epochs. The average precision of all marker detections was evaluated using the test dataset by comparing the markers detected by YOLO with the originally labeled markers. Computations were carried out using a DELL Precision 3660 computer equipped with an Intel Core i9 processor and an NVIDIA GeForce RTX 3080 graphics card. Calculations were performed with Matlab R2021b (The MathWorks Inc., Natick, MA, USA).

## 3. Results and discussion

The total training computation time with our setup was 18 minutes and 35 seconds, yielding a mean accuracy
of 0.7 for the all detections. Initially, this accuracy
may seem low, but it can be attributed to YOLO's
capability to detect markers that were not originally
labeled (Fig 1-D), including phantom markers specular
reflected on the water surface (Fig 1-B), intentionally
left unlabeled. Nonetheless, our observations reveal
that YOLO detection outperforms semi-automatic
method with manual corrections (Fig 1), by detecting
a greater number of markers. Now, focusing only on
markers originally labeled in the test set, we obtained a
sensitivity of 0.97 and a specificity of 0.95 for marker
detection by YOLO. The accuracy of the marker center
location by YOLO, compared to the originally labeled
markers, has a median value of 2 pixels and is within
5 pixels in 95% of cases.

<figure><img src="image_1.png" /><figcaption>Figure 1. Sample marker detection with YOLO.</figcaption></figure>

The black arrow shows a detection of a phantom marker linked to the specular reflection of an existing marker. The white arrow indicates markers that were not labeled on the original image.

However, within the context of the study conducted
here, even the semi-automatic detection of surface
markers required a significant amount of manual cor-
rection work. The utilization of the YOLO algorithm for
detection has demonstrated its relevance and reliability.
Our approach's novelty lies in analyzing not the entire
image, but rather sub-images, which allows for an ade-
quate number of pixels related to the surface markers
while reducing calculation time. Furthermore, employ-
ing a YOLO-v2 type facilitates reasonable computa-
tion times, even with a mid-range computer equipped
with a moderately performing graphics card, all at an

affordable cost. Training conducted here involved data
augmentation and could be considered a potential area
for improvement in the future.

Our preliminary study has limitations. Here we used
videos made on a single horse. We limited the risk of
overfitting, using the images of the 6 cameras that had 6
different viewpoints with variable lighting and contrasts.
This choice was motivated by the fact that already in
this configuration, we were limited in memory capacity
on our workstation. More computational capacity is
needed for a larger database.

4. Conclusions

Based on our preliminary observations, the use of
CNN-YOLO-v2 appears to be well suited for surface
landmarkers detection even in an unconventional envi-
ronment such as underwater. This technique opens
new perspectives on automating the laborious task of
labeling markers on video images. The following work
is to increase the size of databases with videos with
other horses while using augmentation techniques. This
implies computing power that is not in the mid-range
computer capacity.

Funding

This research was funded by the Agence Nationale de
la Recherche (ANR-20-CE19-0016).

Conflict of Interest Statement

The authors have no conflicts of interest to declare.

References

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