# Monocular markerless position tracking of elite amateur boxing fighters in real combat situation

Alexandre Schortgen, Thibault Goyallon, Guillaume Saulière, Antoine Muller, Lionel Revéret

► To cite this version:

Alexandre Schortgen, Thibault Goyallon, Guillaume Saulière, Antoine Muller, Lionel Revéret. Monocular markerless position tracking of elite amateur boxing fighters in real combat situation. Journal of Sports Sciences, 2025, pp.1-15. 10.1080/02640414.2025.2510774. hal-05108725

HAL Id: hal-05108725

https://hal.science/hal-05108725v1

Submitted on 3 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Monocular markerless position tracking of elite amateur boxing fighters in real combat situation

Alexandre Schortgen<sup>a,b,c</sup>, Thibault Goyallon<sup>a</sup>, Guillaume Saulière<sup>b,c</sup>, Antoine Muller<sup>d</sup> and Lionel Revéret<sup>a,*</sup>

## ARTICLE INFO

**Keywords:**

* Multiple Athlete Tracking
* Trajectory Estimation
* Self-supervised Detection
* Boxing Performance
* Computer Vision
* Video Analysis

## ABSTRACT

Markerless video analysis represents an opportunity for conducting efficient in-situ motion analysis of athletes during competitions. From monocular video data, we propose a robust end-to-end method to automatically capture the 2D trajectory of athletes' position on planar ground, even in highly occluded contexts. A tracking-by-detection algorithm is first applied on a short sequence to build a specific contextual dataset ("self-supervision"). These data are subsequently used to train a specific person detector. Afterwards, body anatomical features in image coordinates are identified using human pose estimator. Athletes position is extracted as the midpoint between the feet and converted to metric units through homography. The accuracy of our monocular algorithm was evaluated by comparison with a position trajectories calculated from markerless reconstruction of 3D poses using 11 accurately synchronized and calibrated cameras as reference. The average error was 0.3 m over about 130000 frames at 50 fps. The trajectories of the monocular method and the multiple views reference show an average correlation above 0.9. The robustness of the monocular method was tested in real competition of boxing combats for 18 rounds involving 22 elite fighters. These results open perspectives to provide performance indicators such as ring generalship to the coaching staff with minimal setup.

## 1. Introduction

Quantifying the positioning of athletes during an action is one of the first important key factors in sport analysis. It allows to monitor the physical demand by performing time-motion analysis (López and et al., 2014) and deriving indicators such as exertion indices (Wisbey and et al., 2010) or player loads (SDGTBredt and et al., 2020). Positioning data is also useful to evaluate performance indicators, exposing tactical behaviors (Low and et al., 2020), pacing strategies (Konings and et al., 2016) or technical capabilities. Motion analysis is of prime interest in a wide range of sports from team sports to races or combat sports. In the case of boxing, ring generalship, which is the ability of a boxer to take control of the ring and to dictate the pace of the fight, is an essential key performance indicator in elite amateur boxing. It involves both tactical and technical skills of the boxer. A boxer with good ring generalship is able to understand the positioning and movements of his opponent to define better strategies and impose advantageous fighting ranges (Ashker, 2011). Yet, the study of ring generalship is absent from the literature on performance analysis in boxing. It is mainly due to the lack of data available on the trajectories of boxers in combat, as there is currently no method implemented to collect such data. A variety of sensors are available to monitor the positions of athletes. The assessment of devices such as Global (Coutts and Duffield, 2010), Local

(Fuchs and et al., 2023) or Ultra-WideBand (Leser and et al., 2014) Positioning Systems for characterizing sport specific demands has been extensively studied. However, the deployment of these sensors may prove overly invasive for on-field applications, during which they can hinder the athletes' movements. Alternatively, video capture allows a non-invasive recording of the performance. Therefore, video analysis has become a privileged tool for in-situ performance breakdown in high-level sports. Using video enables a posteriori observations for an objective measurement of performance in complement of the immediate feedback of coaches and athletes. Yet, analytical metrics from video are still often recovered from manual annotation of raw video footages. This tedious and time consuming task limits the applications of video analysis as it compels to a trade-off between the quality and the quantity of information processed.

The development of computer vision has lead to algorithms able to tackle repetitive image processing tasks, bringing opportunities for automating annotation. These algorithms are notably investigated in sports science to detect and locate athletes within the image during sequences of games or fights (Lu, Chen, Little and He, 2018). The positions of multiple human beings can be retrieved by pose estimation models such as OpenPose (Cao and et al., 2017). These models are intrinsically frame-based and, as such, must be coupled to a tracking algorithm to extract the evolution of the poses throughout the video. Trying to match the poses from a frame to the next one proves to

<figure><img src="image_2.png" /></figure>

<footnote><sup>a</sup>INRIA Grenoble Rhone-Alpes, LJK, UMR 5224, Grenoble, France</footnote>

<footnote><sup>b</sup>IRMES — UMR 7329, Institut de Recherche Médicale et d'Epidémiologie du Sport, Université Paris Cité, INSEP, Paris, France</footnote>

<footnote><sup>c</sup>INSEP, Institut National du Sport, de l'Expertise et de la Performance, Paris, France</footnote>

<footnote><sup>d</sup>Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR T9406, Lyon, France</footnote>

<footnote>* Corresponding author</footnote>

<footnote>lionel.reveret@inria.fr (L. Revéret)</footnote>

<footnote>ORCID(s):</footnote>
<figure><img src="image_3.png" /><figcaption>**Figure 1:** Example of an identity switch after a clinching phase when using an off-the-shelf MOT model to track athletes. On the first frame, red and gray boxers are well detected. During clinching (frames 2 & 3) the detections are merged because of the occlusion of the red boxer. After separation (last frame), the tracker have wrongly associated the previous identities switching red and gray detections.</figcaption></figure>

be inoperative in occluded contexts such as boxing bouts. Further identification of the athletes can be carried out directly on the video stream by Object Detection or Multiple-Object-Tracking (MOT) models which had successful implementations such as FairMOT(Zhang and et al., 2021), SORT (Bewley and et al., 2016), ByteTrack(Zhang and et al., 2022), or Trackformer (Meinhardt and et al., 2022).

Implementing field-applicable video analysis solutions requires catering to the minimal video setup available on the competition site, favoring a monocular approach. Furthermore, the solution needs to work in a wide variety of video contexts (e.g. lighting, background), use cases (competition or training), and should work for any boxer and any outfit. Taking these aspects into consideration, the direct implementation of off-the-shelf algorithms is limited. Indeed, Object Detection models require fine tuning due to the video context in order to distinguish specific individuals. Conversely, MOT models can tackle the task of differentiating unknown persons, by associating generic detection from one frame to another. However, in boxing, monocular video data is highly prone to occlusion when an athlete is in the background or during clinching phases, or even because of the presence of the referee, which disrupts tracking algorithms. It ultimately causes identity switches on the results (see Figure 1), which are prohibitory for an accurate position estimation, as the output trajectories are no longer temporally consistent with the athlete they are supposed to represent.

The goal of this work is thus to provide a robust algorithm which estimates the positions of athletes in 2D metric coordinates of the ring space, from monocular video data only, with as little manual intervention as possible to alleviate the workload of coaching staff. The contributions of this work are two-fold:

* we propose an end-to-end automatic method to recover metric trajectories of multiple unknown athletes from monocular video data,

* we propose an evaluation method of the level of error reachable by monocular markerless tracking in highly occluded scenarios of realistic boxing bouts.

# 2. Materials and methods

## 2.1. Materials

The input data consists in monocular footage in HD resolution (1920x1080 pixels) processed at 50 fps. The dataset has been filmed at the training center of the French Federation of Boxing. Test matches have been organized with a setup representative of realistic competition. Athletes were wearing regular outfits, with no additional markers. Combats were split into round lasting for 3min. A referee was present on the ring to monitor the combat. 22 athletes were involved in a total of 18 rounds. For each round, a sequence lasting from 0.5 to 3 minutes was recorded. Over the 18 sequences, a total of 130.000 frames captured and processed by our algorithm.

In all sessions, the outfits of the boxers did not necessarily respect the dress code of official matches (distinct red and blue gears) and could look alike, thus realistically challenging the robustness of the method. All the athletes signed an informed consent form after presentation of the objectives and protocol of the study.

## 2.2. Methods

### 2.2.1. Self-supervised athlete detection : From MOT to object detection

To first overcome the issue of identity switches, we tackle a simpler object detection problem. The main difference with MOT is that we move from trying to find multiple instances of a single class (generic detection of any athlete) to finding unique instances of multiple classes in the image
<figure><img src="image_4.png" /><figcaption>**Figure 2:** Flowchart representing the different steps of our monocular markerless tracking algorithm.</figcaption></figure>

(specific detection of one athlete in particular). However, implementing this detection requires knowing the visual cues about the specific athletes we want to recognize, which are not known beforehand. In practice, the classifier must be fine-tuned on a prior collection of images of the athletes before running the detection.

This is where MOT actually helps us to create a "self-supervised" learning stage for athlete detection (see pipeline in Figure 2). We leverage a MOT algorithm to automatically annotate the first seconds of the video (12s at 50fps, leading to 600 frames). By doing so, we are able to build a unique image dataset specific to the video. Notice that we make the assumption that boxers are well separated on all images during the first 12 seconds of the sequence, given that they start on opposite sides of the ring and that clinches are unlikely to occur that early in the fight (see Figure 11 in Appendix A). This heuristic ensures that the results of the tracking-by-detection are trustworthy. We used ByteTrack framework (Zhang and et al., 2022) to implement this automatic labeling step. The resulting dataset is then fed to a YOLOv5 convolutional neural network (CNN) classifier for training (80%-20% train-test split). For fine-tuning, the CNN was trained by batches of size 32 during 100 epochs. The dimension of the output layer of the network may vary from a video to another as it depends on number of people that were detected at the previous step. The trained CNN is then used to detect the boxers on the remainder of the video. The outputs of the detection are identified bounding boxes in camera pixel coordinates for each frame of the video.

### 2.2.2. Pose estimation

Bounding boxes can be considered as a first cue of the position of the boxers onto the ring coordinates but they suffer from delimitation issues as the box boundaries do not necessarily coincide with the athlete feet (see Figure 3).

Therefore, we define the position of the athletes as the midpoint of their 8 feet keypoints (n°11,14,19,20,21,22,23 and 24) positions, derived from the pose estimation yielded by Openpose Body25 model (Cao and et al. (2017)). We only retain the keypoints with a detection confidence higher than 50%.

<figure><img src="image_5.png" /><figcaption>**Figure 3:** The center of the bottom border (red dots) of the bounding box only gives a rough estimate of the fighter's position (green dots) and can be erroneous depending on their orientation or their stance.</figcaption></figure>

### 2.2.3. Identification

With the classifier results, we can implement frame-by-frame matching of the poses to their corresponding identities. For each frame of the video, we iterate through the identities detected by the first classifier and find, for each one, the pose with the maximum number of keypoints present within its bounding box. The current identity is then assigned to this pose. After re-identification, time-consistent pixel trajectories of the keypoints are extracted for all the athletes identified during the self-supervised detection.

### 2.2.4. Processing

#### Homography transform

The next step is converting the positions from the image pixels coordinate system into the ring metric coordinate system. Using projective geometry, the 2D pixel coordinates in image plane can be expressed as a linear transform of the metric coordinates in the ring plane through a 3x3 homography matrix H as formulated below.

$$ \begin{bmatrix} u \\ v \\ 1 \end{bmatrix} \sim H \begin{bmatrix} x \\ y \\ 1 \end{bmatrix} $$
<figure><img src="image_6.png" /><figcaption>**Figure 4:** Effect of interpolation and low-pass filtering on the estimated trajectories of 2 boxers (red & blue). A: before postprocessing. B: after interpolation. C: after filtering.</figcaption></figure>

~ : the equality holds up to a projective factor

This can be considered as an initial calibration step. The homography matrix $H$ is computed by manually matching the four ring corners - which positions are known in the ring reference frame given its dimensions - with their pixel coordinates counterparts for a single video frame (see Figure 5). The camera setup and the ring location being fixed during the capture session, $H$ is estimated beforehand and is taken as a given input of the algorithm. Once $H$ is defined, the metric coordinates of point in the ring plane can reversely be retrieved from their pixel coordinates from image at any frame using the inverse of the matrix $H$.

However, an homography mapping is only meaningful between planes, typically for the "ground" plane in our case. This is why it is applied only to feet keypoints which are chosen as the definition of the athlete's position. Even if the feet can be sometimes slightly off the ring plane during locomotion, the resolution and the angle of view makes acceptable the assumption that the feet are always closed enough to the ground for the metric mapping to be meaningful. We decided not to correct radial distortion for on-field applications, considering the complexity of calibration protocol to be carried out by non-experts in video capture. Furthermore, in competition context, staff may also not have access to the ring to perform the adequate calibration. Instead, we accept the existence of such distortion and decided

to evaluate our method against properly calibrated cameras, so that the impact on distortion is comprised in the error measurement.

These workarounds reflect the goal of keeping the proposed method to be manageable by the staff autonomously. Camera orientation and zoom factor may be changed for every session at the beginning of the training sessions and a calibration is required. Under these assumptions, the manual identification of the 4 ring corners at the beginning of the session turned out to be a fine compromise between usability and accuracy.

### Filtering

The position trajectories can be sparse and noisy. As shown in Figure 4, a first linear interpolation addresses the problem of missing positions. The trajectories are then smoothed by applying a 4th-order Butterworth low-pass filtering. The optimal cut-off frequency is part of an experimental investigation described below.

## 2.3. Evaluation

### 2.3.1. *Experimental setups*

For evaluation, we have compared the results of the proposed monocular method with the values obtained from a multiple calibrated cameras video system. The monocular video system (S1) is the one installed at the French boxing training center. It represents the minimal setup required to perform the tracking. This system is anchored in the
<figure><img src="image_7.png" /><figcaption>**Figure 5:** Representation of how the homography is defined from the ring corners and how it translates the pixel position into a metric position.</figcaption></figure>

training room, guaranteeing a consistent position which will constitute our monocular point of view for studying the boxers' trajectories. The second video system consists in 11 research-grade cameras (Qualisys Miquis video cameras; 2 megapixel; 60 fps), placed around the ring (S2). The cameras of S2 were hardware-synchronized and accurately calibrated using a L-frame and a T-wand, following built-in procedure from Qualisys Software to retrieve intrinsic and extrinsic parameters. The calibration provides a global reference frame at the center of the boxing platform. These 11 points of view are used to extract the reference data against which we will compare the results obtained from the monocular data collected with S1. Table 1 recaps the image properties of the setups.

<table><caption>**Table 1** Properties of both camera setups</caption><thead><tr><th></th><th>S1</th><th>S2</th></tr></thead><tbody><tr><td>Number of cameras</td><td>1</td><td>11</td></tr><tr><td>Resolution</td><td>1920 × 1080</td><td>1920 × 1080</td></tr><tr><td>Framerate</td><td>50 fps</td><td>60 fps</td></tr><tr><td>Bitrate</td><td>41 Mb/s</td><td>188 Mb/s</td></tr><tr><td>Position</td><td>≈ 5 m from the ring<br>(see view in Appendix)</td><td>around the ring<br>within a 5 m radius</td></tr><tr><td>Focal length</td><td>comprised in</td><td>9 mm</td></tr><tr><td>Aperture</td><td>homography</td><td>f/1.4</td></tr><tr><td>Exposure time</td><td></td><td>4 ms</td></tr></tbody></table>

### 2.3.2. Markerless tracking from multiview video data

Marker-based motion capture was not compatible with realistic conditions of boxing fights, as it would have disturbed the athletes. Instead, we used a multiview markerless approach to track the positions of the boxers. The pipeline for extracting the positions of the boxers from multiview video data is presented in Figure 6. After, applying 2D pose estimation to all the 11 synchronized cameras of S2, the 3D trajectories of the athletes' keypoints are retrieved by

standard reconstruction through Direct Linear Transformation (DLT). These trajectories are expressed in the metric reference frame given by calibration. The 3D feet positions of the boxers are then projected into the ground plane to obtain comparable 2D positions of the boxers' feet. As for the monocular approach, the boxers positions are defined by considering the midpoint between their feet positions.

### 2.3.3. Evaluation procedure

As shown in Figure 6, the multiview markerless tracking yields the same output, namely the 2D trajectories of the midpoint of feet, as our proposed monocular markerless tracking. We can then compare the two quantities to evaluate the difference induced by the restriction to a single point of view. We consider that the results from the multiple views approach will be intrinsically more accurate and as such will be considered as the reference to be compared to. In the rest of the paper, this reference will be referred to as "multi". The trajectories output by the monocular method will be referred as "mono".

To make the comparison possible, the results obtained from S2 (multi) were downsampled to the framerate of S1 (mono) (from 60 to 50 fps). As S1 and S2 were not hardware-synchronized, a post-synchronization was calculated for each fight, by cross-correlating the trajectories obtained on the two systems and correcting the lag with respect to the correlation maximum. Once synchronized, the monocular tracking was assessed by the following error metrics, with respect to the multiview tracking.

### 2.3.4. Error metrics

Comparing monocular and multiview trajectories serves two purposes. On one hand, it assesses the accuracy of our algorithm by quantifying the level of error achievable. On the other hand, it allows to determine the optimal cutoff frequency for low-pass filtering. To do so, we defined metrics expressing the discrepancy between monocular and multiview results (see below). These metrics were computed on the filtered monocular trajectories of the two boxers and then averaged on the 18 sequences, for cutoff frequencies ranging from 0.25 Hz to 5 Hz, with a step of 0.25 Hz. The chosen frequency is the one that gives the best trade-off between the average values of the different metrics.

#### Error on position

For a cutoff frequency f, we compared the monocular trajectories $\mathbf{p}_{\text{mono}}(f, t) = (x_{\text{mono}}(f, t), y_{\text{mono}}(f, t))$ and multiview trajectories $\mathbf{p}_{\text{multi}}(t) = (x_{\text{multi}}(t), y_{\text{multi}}(t))$ by evaluating the mean difference (MD) calculated as the euclidean distance between the estimated positions over an entire round.

$$MD(f) = \frac{1}{N_{frames}} \sum_{t \in frames} \| \mathbf{p}_{\text{mono}}(f, t) - \mathbf{p}_{\text{multi}}(t) \|_{2}$$

Furthermore, we evaluated the mean absolute errors (MAE) along the x and y axes to investigate if the accuracy of our method varies with the direction considered.
<figure><img src="image_8.png" /><figcaption>Figure 6: Flowchart illustrating the method for multiview markerless tracking, considered as reference in the evaluation study.</figcaption></figure>

$$MAE_x(f) = \frac{1}{N_{frames}} \sum_{t \in frames} |x_{mono}(f, t) - x_{multi}(t)|$$

$$MAE_y(f) = \frac{1}{N_{frames}} \sum_{t \in frames} |y_{mono}(f, t) - y_{multi}(t)|$$

### Error on distance

Having access to the frame-by-frame position of the athletes, we derived the distance covered by each athlete along the trajectory. Looking at this new metric is relevant for investigating an optimal filtering frequency because, as formulated by Ranacher and et al. (2016), the uncertainty on position tracking has a direct impact on the distance estimation.

The displacement of a boxer from one frame to another is directly derived from the backward difference of the positions. Hence, the distance covered from one frame to another is :

$$d(t) = \|\mathbf{p}(t) - \mathbf{p}(t-1)\|_2 = \sqrt{(x(t) - x(t-1))^2 + (y(t) - y(t-1))^2}$$

The total distance covered during a round is obtained by summing over all the frames:

$$D = \sum_{t=1}^{N_{frames}} d(t)$$

The relative error on the total distance (RETD) between monocular and multiview estimated distances is then defined by:

$$RETD(f) = \frac{D_{mono}(f) - D_{multi}}{D_{multi}}$$

### Agreement analysis

Intraclass correlation coefficients (ICC) between monocular and multiview estimated positions was calculated for each of the 18 sequences. In addition, a Bland-Altman analysis (Martin Bland and Altman, 1986) was conducted in order to determine the limits of agreements (LOA) between monocular and multiview results, giving the interval/range of error between both positions at a 95% confidence level.

# 3. Results

## 3.1. Optimal frequency

Figures 7 and 8 show the impact of the filtering on both position and distance estimation. On the one hand,

while a minimal mean difference is found for a frequency of 1 Hz, we only observe a difference of less than 1 cm for all frequencies between 0.5 Hz and 5 Hz. As this difference is not significant, no optimal frequency for the position error can be deduced from this criteria.

<figure><img src="image_9.png" /><figcaption>Figure 7: Mean Difference between monocular and multiview results for each sequence for cutoff frequencies ranging from 0.25 Hz to 5 Hz. (dashed: average over 18 sequences, gray area: standard deviation interval)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure 8: Relative error on total distance between monocular and multiview results over for each sequence for cutoff frequencies ranging from 0.25 Hz to 5 Hz. (dashed: average over 18 sequences, gray area: standard deviation interval)</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Figure 9:** Top: Tracking results (left: x position, right: y position) from monocular and multiview methods for both boxers. Bottom: Corresponding Bland-Altman plots assessing the agreement between both methods for x (left) and y (right) positions of both boxers.</figcaption></figure>

On the other hand, when looking at the total distance covered along the trajectory, a minimal error is found for a frequency of 2.25 Hz, frequency we chose as the optimal parameter for low-pass filtering.

For this cutoff frequency, the mean difference between monocular and multiview estimations was, on the average of 18 round, 29.6 cm. When taking directions into account, we see that MAE was greater along the y axis than along the x-axis (resp. 25.7 cm and 14.0 cm in average). When considering the error on the distance covered, we found a 8.8% difference between both methods.

<table><caption>**Table 2** Values of the errors for a cutting frequency of 2.25 Hz.</caption><thead><tr><td>Error</td><td>Mean</td><td>SD</td></tr></thead><tbody><tr><td>MD (cm)</td><td>29.6</td><td>11.1</td></tr><tr><td>MAE<sub>x</sub> (cm)</td><td>14.0</td><td>4.4</td></tr><tr><td>MAE<sub>y</sub> (cm)</td><td>25.7</td><td>11.0</td></tr><tr><td>RETD (%)</td><td>8.8</td><td>9</td></tr></tbody></table>

## 3.2. Agreement results

The results of the Bland-Altman analysis are summed up in Table 3. The average ICC for over the 18 sequences and both boxers were respectively 0.97(±0.03) and 0.88(±0.12) for the x and y positions. While similar average biases were

found along x (-6 cm) and y (-5 cm) directions, lower limits of agreement (LOA) for 95% of the estimations were found along the x (35 cm) than the y (75 cm) directions. An example of Bland-Altman plot for both boxers positions is given for a 3 min sequence in Figure 9, showing the larger bounds of agreement for the y position. All bland-Altman plots for each sequence are given Appendix D.

<table><caption>**Table 3** Bland-Altman analysis for a cutting frequency of 2.25 Hz (average on 18 sequences)</caption><thead><tr><td>Axis</td><td>CC</td><td>Bias (cm)</td><td>LOA (1.96 × SD) (cm)</td></tr></thead><tbody><tr><td>x</td><td>0.97(±0.02)</td><td>-6(±6)</td><td>35(±13)</td></tr><tr><td>y</td><td>0.88(±0.12)</td><td>-5(±12)</td><td>75(±32)</td></tr></tbody></table>

## 4. Discussion

The proposed algorithm provides an end-to-end method to automatically estimate the trajectory of unknown boxers onto the ring in metric coordinates. The Figure 12 in Appendix B provides a snapshot for all sequences used for validation, showing the variability of appearances of the boxers detected by our algorithm. Most of the athletes were instructed to wear red and blue outfits. Yet, when boxers wore similar outfits, our method still gives a successful tracking without needing a posterior correction. Likewise, our
<figure><img src="image_12.png" /><figcaption>**Figure 10:** Influence of filtering on position tracking and covered distance. (A: Trajectory. B: Error on position. C: Cumulative distance - Cutoff frequencies: top=4.5 Hz, middle=1.5 Hz, bottom=0.5 Hz). As shown here along the x-axis, the smoothing only has a little impact on the average error (column B.). Yet, the calculated distance is very sensitive (column C.).</figcaption></figure>

algorithm avoids the problem of identity switches present with MOT. As can be seen on Figure 13 in Appendix C, when occlusions occur and detection punctually fail, our algorithm correctly retrieve the identities of both boxer after they separate.

While a special focus is given to boxing applications, the method may still holds efficient for a variety of other sports due to its robustness to different contexts. It would specifically profit to disciplines such as team sports, when the video context is highly occluded by players looking alike. Notice that our method focused on automating as much as possible the tracking pipeline, to limit the burden on the coaching staff. Currently, the only manual operation required is the calibration of the homography, which was carried out by a light point-and-click interface for only 4 corners. This simplicity of use, together with a compact video setup, enables on-field applications.

## 4.1. Optimal filtering parameter

The Figure 7 shows that the choice of the cutoff frequency has limited effect on the average Mean Difference between monocular and multiview trajectories. This could be because smoothing mostly corrects one-off discrepancies (noise) which do not weigh much in calculating the average error, given that their occurrence is very low compared to the number of points in a 3-minute trajectory (see middle column of figure 10). Conversely, these occasional errors of greater amplitude lead to punctual overestimation of the distance covered, the total of which can reach an order of magnitude which is significant (± 50% of the calculated

distance - see right column of Figure 10). As a consequence, the frequency chosen for data smoothing is the one which minimizes the RETD metric, i.e. 2.25 Hz (see Figure 8).

The optimal cut-off frequency found of 2.25 Hz is typically lower than those used to process trajectories in motion analysis (Schreven, Beek and Smeets, 2015), especially in a sport with such dynamic movements as boxing. However, when focusing on tracking the position of the boxers, the specific back and forth footwork of boxers results in a much slower overall motion of their feet midpoint than their actual kinematics, hence explaining a lower frequency needed for low pass filtering. Furthermore, the same range of cutoff frequency (3 Hz) was used by Figueira, Gonçalves, Folgado, Masiulis, Calleja Gonzalez and Sampaio (2018) when assessing the accuracy a basketball indoor tracking system.

## 4.2. Level of accuracy

Our monocular algorithm yields an average Mean Difference to multiview triangulated results of 29.6 cm, which represents 4.9% of the ring dimensions. To the best of our knowledge, only dos Santos Banks, Santiago, da Silva Torres, de Oliveira and Moura (2024) have addressed multiperson tracking in fighting sports (taekwondo). Their 2D position reconstruction showed similar accuracy with a RMSE of 26.9 cm and 24.3 cm (around 3% of the area dimension) for respectively the x and y coordinates of their two-dimensional positions, when compared to marker-based motion capture tracking. Previous results of position tracking in tennis (Lara, Vieira, Misuta, Moura and de Barros (2018)) also gave the same ranges of accuracy, with a RMSE on position of 36 cm
compared to manual annotation. The agreement analysis
showed a low average bias when compared to the limits of
agreements, meaning that our monocular tracking is accurate
but sensitive to the noisiness of pose estimation and missed
detections caused by occlusions. The main source of error in
our tracking comes from the occlusion of either boxer, which
causes prolonged trajectory interpolations which drift away
from the actual trajectory (such drifts can be seen as outliers
on the Bland-Altman plots). Nonetheless, no upper limit in
the consecutive number of missing frames was considered
for interpolation. In average, the maximum occlusion for a
boxer lasted for 102 frames (i.e 2s). Considering that occlu-
sion tends to happen during less dynamic phases (clinches),
it appeared short enough to still apply interpolation. Boxing
represents a particularly challenging context regarding these
occlusions as they are occur frequently and for an extended
duration during clinching.

The difference in accuracy regarding the plane coordi-
nates of our trajectory is explained by the point of view of
our monocular capture (see Figure 3). Given the reference
axes defined by our homography (see Figure 5), the x and y
coordinates correspond respectively to lateral and longitudi-
nal directions with respect to the optical axis of the camera,
which causes higher errors after homography along the y
coordinate. Such discrepancies in accuracy between both
axes of the position were also found in tennis (Lara et al.,
2018). Furthermore, the agreement analysis showed similar
biases in both directions but different limits of agreement,
indicating that tracking on the y axis is more sensitive to
uncertainty on pose estimation and object detection results
and more sensitive to occlusions. This influence of camera
perspective on the tracking accuracy may need to be taken
into account if the motion of interest is unidirectional or if
tracking focuses on a specific direction.

4.3. Limits

Although the results obtained are satisfactory enough to
provide exploitable trajectories for the analysis of fights, we
have identified some limits to the algorithm. When the fight-
ers look very alike (similar colors of outfit), the detection
accuracy may decrease as it is harder for the model to dis-
criminate between both identities. In this case, increasing the
number of frames of the initial learning step could improve
the results of detection, as it increases the corresponding
training dataset. The size of the training sample must still be
short enough to guarantee that no switch occurs on the MOT
inference. The lighting of the scene may also disrupt the
detection results. For instance, in a competition arena where
the room is dark with lighting directed towards the centre
of the ring, different zones of the image have contrasting
brightness levels. In such case, the appearances of the boxers
can strongly differ from one zone to another, resulting in
missed detection. As the identification learning step occur on
the beginning of the fight, the classifier is fitted on images
of the fighter leaving their corners and moving toward the
center of ring. As a result, the training dataset for each boxer

includes only a fraction of the positions he may occupy in the
ring. The issue of over-fitting then arise and the detection
accuracy tends to decline when the boxer is in peripheral
areas of the ring.

Finally, we have assumed a constant point of view to
limit the calibration of the homography to a single frame
as it is currently done by hand. Automatically detecting the
corners of the ring could be considered for this operation as
well. This would make it possible to compensate for slight
camera movements, but might also introduce another signif-
icant source of error. In any case, it would be impossible
to process edited video streams from tournament broadcasts
because of the wide variations in terms of camera angle and
zoom.

4.4. Guidelines

The computation time is around 30 min per round with
high-grade GPU material (NVidia Tesla V100 32GB). This
is still incompatible with real time analysis for immediate
feedback to the coaching staff. Nevertheless, it was short
enough to process all the videos of the day overnight. It was
satisfying enough to produce an analysis of the tournament
on a short notice.

Although our algorithm was designed to work without
prior knowledge of the video environment, it is even more
effective in a given context (e.g given gears/helmets/gloves
colors, specific gym background). Pre-training can be used
for the classifier to take advantage of the context by bypass-
ing both automatic annotation and classifier training, which
is the most time-consuming stage for computation, and
directly inferring the detection. It is thus possible to halve
the computation time required to output the trajectories.
Pre-training should be made on trial sequences prior to the
capture and in the same video context in order to be effective.

As the algorithm is self-supervised, it detects all the peo-
ple present on the video during the initial training sequence.
As a result, some unintended subjects present around the ring
or in the background may interfere with the correct detection
of the boxers. To prevent possible mix-ups, improve detec-
tion quality and reduce computing time, it is possible to pre-
process the videos by blurring or masking the areas of the
image of no interest (outside the positions that the athletes
can reach).

5. Conclusion

We proposed a full pipeline for robustly estimating the
trajectory of multiple athletes in real performance context
from a monocular video stream. This method combines
Multiple-Object Tracking and Object Detection models to
identify the athlete throughout the video while not requir-
ing preliminary manual training of the model. This ap-
proach prevents the occurrence and propagation of identity
switches, thereby guaranteeing the accuracy of the estimated
trajectory. Attention was paid to limit the number of con-
strains on video context, allowing our method to work in
a large variety of scenes possible and making this solution
field-applicable. For instance, no assumption was made on
the athletes' appearance nor on the type of field observed. Consequently, while a particular application to boxing has been given in this paper, the method remains valid whatever the sport considered, and could be applicable to any discipline for which the playing field can be covered with sufficient precision by a monocular view. More precisely, our method is specifically relevant in occluded contexts were simple MOT inference is not possible, such as contact sports of team sports.

Our algorithm provides position data suitable for ring generalship characterization in both training and competition situations. For practice, our video analysis system would make it possible to qualify the athlete's response to certain combat scenarios or types of boxing, and to check the integration of technical elements on positioning, and their reinvestment in combat situations. Deploying it in competition would enable to provide feedback on short notice, to profile opponents for competitive analysis, and on a larger scale and to correlate positioning data with judges' results to investigate the determinants of boxing performance.

### Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of CERSTAPS (IRB00012476-2023-29-08-265, 29 Aug 2023).

### Funding details

This work was supported by ANR PPR STHP 2020 (project PerfAnalytics, ANR 20-STHP-0003) and INRIA participation to Sciences2024 (funding of doctoral thesis).

### Informed Consent Statement

Written informed consent was obtained from the subjects to publish this paper.

### Disclosure statement

The authors report there are no competing interests to declare.

### Acknowledgments

This research is part of a partnership with the Fédération Française de Boxe. The authors thank coordinator Lionel Brezéphin, Chloé Lesenne and all the subjects for their participation to this study.

### CRediT authorship contribution statement

**Alexandre Schortgen:** Conceptualization, Methodology, Software, Data curation, Writing – original draft. **Thibault Goyallon:** Investigation, Data curation, Software, Writing – review & editing. **Guillaume Saulière:** Investigation, Data curation, Writing – review & editing. **Antoine Muller:** Investigation, Data curation, Writing – review & editing. **Lionel Revéret:** Supervision, Investigation, Data curation, Writing – review & editing.

### References

Ashker, S.E., 2011. Technical and tactical aspects that differentiate winning and losing performances in boxing. International Journal of Performance Analysis in Sport.

Bewley, A., et al., 2016. Simple online and realtime tracking, in: 2016 IEEE International Conference on Image Processing (ICIP), IEEE. doi:10.1109/icip.2016.7533003.

Cao, Z., et al., 2017. Realtime multi-person 2d pose estimation using part affinity fields. CVPR.

Coutts, A.J., Duffield, R., 2010. Validity and reliability of gps devices for measuring movement demands of team sports. Journal of Science and Medicine in Sport 13, 133–135. doi:https://doi.org/10.1016/j.jsams.2008.09.015.

Figueira, B., Gonçalves, B., Folgado, H., Masiulis, N., Calleja Gonzalez, J., Sampaio, J., 2018. Accuracy of a basketball indoor tracking system based on standard bluetooth low energy channels (nbn23®). Sensors 18, 1940. doi:10.3390/s18061940.

Fuchs, P.X., et al., 2023. Accuracy of a local positioning system for time-series speed and acceleration and performance indicators in game sports. Sports Biomechanics 0, 1–15. doi:10.1080/14763141.2022.2110513.

Konings, M.J., et al., 2016. Pacing behavior and tactical positioning in 1500-m short-track speed skating. International journal of sports physiology and performance 11 1, 122–9.

Lara, J.P.R., Vieira, C.L.R., Misuta, M.S., Moura, F.A., de Barros, R.M.L., 2018. Validation of a video-based system for automatic tracking of tennis players. International Journal of Performance Analysis in Sport 18, 137–150. doi:10.1080/24748668.2018.1456886.

Leser, R., et al., 2014. Accuracy of an uwb-based position tracking system used for time-motion analyses in game sports. European Journal of Sport Science 14, 635–642. doi:10.1080/17461391.2014.884167.

Low, B., et al., 2020. A systematic review of collective tactical behaviours in football using positional data. Sports medicine 50, 343–385. doi:10.1007/s40279-019-01194-7.

Lu, K., Chen, J., Little, J.J., He, H., 2018. Lightweight convolutional neural networks for player detection and classification. Computer Vision and Image Understanding 172, 77–87. URL: https://www.sciencedirect.com/science/article/pii/S1077314218300341, doi:https://doi.org/10.1016/j.cviu.2018.02.008.

López, M.C., et al., 2014. Time-motion analysis procedure in team sports: Example for youth basketball. Strength and Conditioning Journal 36, 71–75. doi:10.1519/SSC.0000000000000061.

Martin Bland, J., Altman, D., 1986. Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet 327, 307–310. URL: https://www.sciencedirect.com/science/article/pii/S0140673686908378, doi:https://doi.org/10.1016/S0140-6736(86)90837-8. originally published as Volume 1, Issue 8476.

Meinhardt, T., et al., 2022. Trackformer: Multi-object tracking with transformers. arXiv:2101.02702.

Ranacher, P., et al., 2016. Why gps makes distances bigger than they are. International Journal of Geographical Information Science.

dos Santos Banks, L., Santiago, P.R.P., da Silva Torres, R., de Oliveira, D.C.X., Moura, F.A., 2024. Accuracy of a markerless system to estimate the position of taekwondo athletes in an official combat area. International Journal of Performance Analysis in Sport 0, 1–16. doi:10.1080/24748668.2024.2321738.

Schreven, S., Beek, P.J., Smeets, J.B., 2015. Optimising filtering parameters for a 3d motion analysis system. Journal of Electromyography and Kinesiology 25, 808–814. URL: https://www.sciencedirect.com/science/article/pii/S1050641115001182, doi:https://doi.org/10.1016/j.jelekin.2015.06.004.

SDGTBredt, et al., 2020. Understanding player load: Meanings and limitations. Journal of human kinetics 71, 5–9. doi:10.2478/hukin-2019-0072.

Wisbey, B., et al., 2010. Quantifying movement demands of afl football using gps tracking. Journal of Science and Medicine in Sport 13, 531–536. doi:https://doi.org/10.1016/j.jsams.2009.09.002.

Zhang, Y., et al., 2021. Fairmot: On the fairness of detection and re-identification in multiple object tracking. International Journal of
Monocular markerless position tracking of elite amateur boxing fighters in real combat situation

Computer Vision 129, 3069–3087.

Zhang, Y., et al., 2022. Bytetrack: Multi-object tracking by associating every detection box.
# APPENDIX

## A. Beginning of a fight

<figure><img src="image_13.png" /><figcaption>**Figure 11:** Frames extraction on the 12 first second of a combat. At the beginning, boxers stand on opposite corners of the ring and move towards the center to start the fight. During this period, boxers are well distinguished in most of the frames and clinches are unlikely occurs, so that MOT models are suitable for detecting the different persons present in the video.</figcaption></figure>
## B. Overview of the sequences

<figure><img src="image_14.png" /><figcaption>**Figure 12:** Frame extraction of the 18 sequences constituting our validation dataset. Different weight categories were represented. On two frames of the top row, we can see that the boxers wear similar outfits.</figcaption></figure>
C. Examples of occlusions

<figure><img src="image_15.png" /><figcaption>**Figure 13:** Frame extractions from 4 fights (one for each row) showing how our algorithm behaves during occluded sequences. When occlusion occurs, at least one boxer goes undetected. After the boxers are disentangled, they are correctly detected once again, without identity switch. The sequence at the top is the same as in the Figure 1 and shows the improvement made by our method compared to the sole inference of MOT alone.</figcaption></figure>
D. Bland-Altman plots on x and y positions
for all the sequences of the evaluation
dataset

<figure><img src="image_16.png" /></figure>
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>
<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>
<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>
<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>
<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>
<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>
<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>
<figure><img src="image_33.png" /></figure>