FOOTBALLTrace: An AI-Based System for Football Player Tracking

with Occlusion Detection and Trajectory Correction

Abdelrahman H. Mostafa

, Muhammad A. Rushdi

1,2 b

, Tamer A. Basha

and Khaled Sayed

Department of Systems & Biomedical Engineering, Faculty of Engineering, Cairo University, Giza, Egypt

Department of Computer Science, New Giza University, Giza, Egypt

Department of Electrical & Computer Engineering and Computer Science, University of New Haven, West Haven, CT, U.S.A.

Keywords: Multiple Object Tracking, YOLO, Soccer Player Tracking, Football Analytics.

Abstract: Data analytics have had a significant impact on tactical and workload planning in football. Football data is

divided into two categories: event data, which captures on-the-ball events like passes and shots, and tracking

data, which captures off-the-ball movements. However, traditional methods of collecting tracking data are

expensive and inconvenient. Recently, AI solutions have emerged as low-cost and user-friendly alternatives

to track players' movements from video streams. This paper introduces FOOTBALLTrace, an end-to-end AI

system for tracking multiple football players from a panoramic game view. The system also incorporates a

novel algorithm that detects potential occlusion events and ensures trajectory continuity for occluded players.

The workflow involves five stages: panoramic view creation, player detection, player ID association,

occlusion detection, and trajectory correction. The system utilizes YOLOv7 for multiple object detection and

employs a pre-trained deep affinity network to assign unique IDs to players throughout the game. Occlusion

detection and trajectory correction are achieved by extracting geometric features from discontinuous player

trajectories. The system's performance was evaluated on full-length video data of a football game, with

occlusion events manually extracted for training and testing the occlusion detection and trajectory correction

algorithm. The system achieved an 87.5% trajectory correction rate for occluded trajectories.

1 INTRODUCTION

Football industry has been one of the most emerging

industries in the world, with significant impact on

economies of both developed and developing

countries (Zhang, 2018; Dejonghe, 2007). For

example, the English Football Premier league has

made revenues of around 5.7 Billion GBP in season

2022/2023 (Premier League Clubs Revenue By

Stream, 2023) . This emerging importance of the

football industry has grabbed the attention of the

scientific community to utilize data in performance

management, training-workload management, and

optimizing the tactical training of professional

players (Arnason, et al., 2004).

Data-driven football analytics require tools to

collect data from football games efficiently, to be

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used in tactical analysis and training-load planning

(Pifer, 2019). Football data is classified according to

the player’s ball possession into event data and

tracking data (Rein, 2016). Event data describes “on-

the-ball” events such as passes, shots, duels, and

goals. Event data is collected by manual operators

with the aid of a special tagger software such as

TAGG-MAKER (Tagg Maker, 2023) and ONCE

(Once- Video Analysis, 2023).

On the other hand, tracking data includes all other

“off-the-ball” events and players’ positional data,

encompassing all the necessary information for

analysts and coaches to design training loads and

tactical plans (Forcher, 2018; De Silva, 2018).

Conventional tracking data acquisition is done by

using wearable technologies such as GPS vests or

wrist-bands with GPS modules (Tierney, 2019).

194

Mostafa, A., Rushdi, M., Basha, T. and Sayed, K.

FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction.

DOI: 10.5220/0012226800003587

In Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2023), pages 194-201

ISBN: 978-989-758-673-6; ISSN: 2184-3201

However, those conventional tracking approaches are

facing some challenges related to their cost, players’

safety, and players’ acceptance to wear them during

the game (Edgecomb, 2006). These challenges have

demanded the development of cost-effective,

contactless alternatives to acquire tracking data

during football games.

Filming football games with high-resolution

cameras paved the way towards computer-vision-

based approaches to track players without the need

for extra wearables (Thomas, 2017). Vision-based

tracking data acquisition from the football game feed

has shown promising results. However, certain in-

game scenarios are currently hindering the practical

use of this approach. Occlusion is one of the in-game-

scenarios that occurs frequently during a game

whenever players are in a duel or they move across

each other (Sabirin, 2015). Detecting occlusion

events and correcting the trajectories of occluded

players will improve the performance of vision-based

tracking system and output more usable tracking data.

Many multiple object tracking (MOT) paradigms

have been introduced over the last two decades such

as the tracking by detection paradigm (Andriluka,

2008) which starts with object (e.g., player) detection

in a specific video frame, and constructs a feature

vector for each detection that describes each detected

object. Object detection is the input stage for any

MOT pipeline, where the choice of the detector that

has the best accuracy and highest speed is the key for

efficient detections (Luo, 2021). Recently, deep

neural networks architectures have shown significant

performance in learning features and detecting

objects, and are now the base of any state of the art

object detector (Zhao, 2019). Detection is repeated

for all objects in each frame, then frame-to-frame

features are compared using similarity computation

algorithms and objects with highly similar features

are joined by a unique ID between frames. The

process of associating objects sharing the same ID

over multiple frames leads to the construction of an

object’s trajectory (Sun, 2019).

Effective tracking algorithms rely on accurate

modeling of detected objects in each frame. There are

two main approaches for modeling: appearance

modeling and motion modeling (Luo, 2021).

Appearance modeling is an object-descriptive

approach that efficiently models objects in local

regions. However, when similar objects appear in

local regions, appearance modeling does not achieve

the best performance. On the other hand, motion

modeling adopts probabilistic approaches to model

the dynamic behavior of the player (object) and

predicts player’s future locations according to a

statistical model. Both appearance and motion

modeling approaches lack the ability to detect and/or

correct the trajectories of occluded objects (Luo,

2021).

Figure 1: FOOTBALLTrace Workflow.

In this paper, we adopt the appearance modeling

approach to propose an end-to-end AI-based system

(Figure 1) called FOOTBALLTrace for acquiring

low-cost, contactless tracking data for football

analytics. Our system films the game with three fixed

cameras with 25 frames per second, stitching the three

camera views to create a panoramic view of the game,

utilizes a state-of-the-art real-time object detector,

YOLOv7, and a deep affinity estimation network to

assign unique IDs for detected players throughout the

entire game. Additionally, we introduce a novel

algorithm for occlusion detection and trajectory

correction to enhance the practicality of our system.

2 DATASET DESCRIPTION

Figure 2: Camera setup for panoramic view creation.

We used video data of a football game recorded with

three 4K resolution Go-Pro cameras with wide view

and 30% overlap between each camera view as seen

in Figure 2. The 3 views are then stitched together

using an image registration algorithm (Szeliski, 2007)

to create a panoramic view of the pitch (depicted in

Figure 3). The game video recording and panoramic

view creation are carried out by KoraStats, Egypt

(KORASTATS, 2023). The game is filmed at the Air

Defense Stadium in Cairo, Egypt. The tracking

coordinates are mapped to a pitch top view using

Homography transformation between the pitch

coordinates in the camera view and the corresponding

coordinates in the pitch top view.

FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction

195

Figure 3: Panoramic View of the Pitch after Stitching.

Additionally, we created a dataset of 40 occlusion

cases comprising 80 discontinued trajectories. We

created this dataset by visually examining the

tracking output on the panoramic view video. An

occlusion case is defined by start and end timestamps,

during which the trajectories of both the occluding

and occluded players are plotted in 2D, and visually

inspected as depicted in Figure 4. In the occlusion

case shown in Figure 4, the trajectories of the

occluded and occluding players exhibit sharp turns.

Other occlusion scenarios may result in the occluded

player’s ID disappearing and a new ID appearing for

the same player after the occlusion ends.

Figure 4: (Left): Occluded Player with ID = 292. (Right):

Player 292 Trajectory with a sharp turn at the blue point

which show the time of occlusion.

To create a structured data frame for our trajectory

correction algorithm, we split each occlusion case

into two records: one for the occluded player and one

for the occluding player as illustrated in Table 1.

Table 1 includes the start and end timestamps of the

occlusion case, the ID of the occluded/occluding

player (before occlusion), and the coordinates (X and

Y) of the bounding box that indicates a detected

object (i.e., player). The range of upper and lower

bounds in both X and Y directions are set to be 15%

of the width of pitches’ tactical zones (Kim, 2019).

Table 1: Sample occlusion data record.

Timestamp

Player

X-axis Y-axis

Start End

Lower

Boun

Upper

Boun

Lower

Boun

Upper

Boun

00:02 00:07 17 264 269 201 206

3 METHODS

Our proposed player tracking system,

FOOTBALLTrace, consists of five main stages:

panoramic view creation, player detection, player ID

association, occlusion detection, and trajectory

correction. The panoramic view creation step is

performed at KoraStats (KORASTATS, 2023) where

real-time stitching between the three camera views is

executed during game filming. We then apply the

object detection algorithm, YOLOv7, to generate a

bound box surrounding each detected player in every

frame. The player ID association is performed using

a Deep Affinity Network (DAN) (Sun, 2019) that

accepts two frames as an input to associate objects

appearing in both frames.

However, due to frequent interaction among

players during the game, occlusion events can occur,

leading to inaccurate player ID associations and

disrupted trajectories. For instance, when players A

and B are in close proximity and occlude each other,

their IDs may get swapped (e.g., Player “A” gets

assigned the ID “B” and player “B” gets assigned the

ID “A”). Our novel algorithm detects occlusion

events and utilizes geometrical trajectory analysis to

identify sharp trajectory turns, indicating the

occurrence of occlusion. Finally, it performs

trajectory correction by re-connecting the corrected

tracks

3.1 Player Detection

In this study, we utilized one of the state-of-the art

real-time detectors, YOLOv7 (You Only Look Once)

(Wang, 2023). YOLOv7 surpasses all known object

detectors in terms of both speed and accuracy when

applied to videos with a frame rate from 5 to 160 FPS.

Additionally, it exhibits the highest accuracy of

56.8% Average Precision (AP) among all known real-

time object detectors (Wang, 2023). YOLOv7 was

trained by Wang et. al (Wang, 2023) on the widely

recognized Microsoft Common Objects in Context

(MS COCO) dataset (Lin, 2014). The learned weights

icSPORTS 2023 - 11th International Conference on Sport Sciences Research and Technology Support

196

on the MS COCO were used in this study to detect

players in game videos with a frame rate of 25 FPS.

The output of YOLOv7 consists of bounding boxes

for each detected player in each frame, which are

employed by the association network to construct

continuous trajectories.

Figure 5: Sample of Players’ Detection.

3.2 Player ID Association

To construct a player trajectory throughout the entire

game, we utilized a Deep-Affinity-Network (DAN)

(Sun, 2019) to associate the player’s ID across

consecutive frames. DAN takes two consecutive

frames as input and performs object modeling to

associate similar objects based to their affinity

estimation score; this score is computed using dot

product of the estimated feature vectors for detecting

bounding boxes as described in (Sun, 2019) . The

DAN architecture is divided into two deep network

components: the feature extractor network and the

affinity estimator network.

3.2.1 Feature Extractor Network

The feature extractor network is based on the Visual

Geometry Group (VGG) architecture (Simonyan,

2014). It models the appearances of objects (players)

by utilizing geometric-based convolutions. VGG

extracts appearance features of players at multiple

levels of abstraction (i.e., multiple scales). The

feature extractor produces a 520-dimensional feature

vector for each detected object in each frame. These

feature vectors are then concatenated with feature

vectors from other frames to form a frame-to-frame

feature matrix for player ID association (Sun, 2019).

3.2.2 Affinity Estimator Network

The feature matrices from the two input frames are

then fed into the affinity estimator network, which

performs exhaustive pairing permutations of features

to estimate the similarities and relationships between

players across different frames. The DAN

incorporates a specialized loss function for affinity

prediction, which ensures a consistent low error in

data association (Sun, 2019). The association

algorithm is robust to short-term occlusions and

capable of accurately tracking players entering and

exiting the scene.

3.3 Occlusion Detection

In occlusion events, the object detector may fail to

distinguish between the two occluded players,

resulting in association failure and potentially

associating the occluded player ID with the occluding

player ID.

False ID associations can be classified into two

types; 1) Swapping of IDs between the occlud(ing)

and occlud(ed) players, or 2) Assigning new ID for

the occluded player as if it were a new player entering

the scene. The type of false association during

occlusion depends on the duration of the occlusion.

The parameter that defines the duration of a long

occlusion is denoted as δ

. This parameter is defined

in the affinity estimation part of the network,

specifying the number of previous frames the network

considers during association (Sun, 2019). This means

that if the occlusion lasts for a duration longer than δ

(which is set to 15 frames in our study), then the

occluded player will be considered as a new player

and will be assigned a new ID. If the occlusion lasted

less than δ

, then the occluded player either retains the

same ID before swapping, or the IDs are swapped

between the occluded and the occluding player, and

that depends on the networks ability to compute

affinities.

Figure 6: Occlusion suggestion for player ID 19 (green box)

and player ID 17 (blue box) with the suggestion bounding

box drawn on the pitch zone where occlusion case has

occurred.

We have developed an occlusion detection

algorithm to aid in identifying possible occlusions

during the game. The algorithm utilizes a sliding

window of 50 frames (2 seconds) to the algorithm

with a 50% overlap between consecutive windows

where each window contains the coordinates and IDs

FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction

197

of the detected players. The algorithm then computes

a mutual Euclidean distance matrix for players in

each frame within the window. A possible occlusion

is identified if the distance between two or more

players is less than or equal to a proximity threshold

(Ʈ). In this work, Ʈ is set to 4 for the purpose of

reproducibility. Optimization of this parameter is left

for future work.

If a possible occlusion occurs for a duration equal

to or more than 50% of the window length, the IDs of

these two players are marked as possible occlusions.

A bounding box is drawn around the pitch zone where

the possible occlusion case took place, along with

printing the time stamp (in minutes and seconds)

corresponding to this case (Figure 6). An operator can

refer to the original game camera feed and confirm

the possible occlusion before it is input to the

trajectory correction algorithm.

3.4 Trajectory Correction

Figure 7: The effect of Savitzky-Golay smoothening filter

on a trajectory. (Left): Trajectory before smoothening.

(Right): Smoothed trajectory.

We introduce a novel trajectory correction algorithm

to re-connect disrupted trajectories in confirmed

occlusion events (Algorithm 1). The algorithm

utilizes geometrical analysis of trajectories (tracks)

within a 5-second window around the occlusion

event. However, this step is preceded by a trajectory

pre-processing phase aimed at smoothing out the

trajectory by eliminating noise in the player’s

movement and unwanted trajectory breaks, as

depicted in Figure 7.

To smooth out the trajectory, we employed the

Savitzky-Golay (SG) filter (Schafer, 2011) . The SG

filter applies a polynomial of order (n) to a subset of

trajectory points within a defined window (w).

Smoothed trajectories are then resampled using a step

(s). The smoothed and resampled trajectory is used

for the trajectory correction step described in Figure

8. We choose a point (P) on the trajectory, which is

Data: Disrupted trajectories of the players

involved in a manually confirmed occlusion

case.

Result: Corrected trajectories of the players

involved in a manually confirmed occlusion

case.

For point P(i) in the disrupted trajectory array

T do

calculate v1 and v2 and angle between them

v1 = P(i) – P(i-1);

v2 = P(i) – P(i+1);

Angle = dot (v1, v2) / (norm(v1)

*norm(v2));

Append calculated angle to angles array

Find index of minimal angle in Angles array

min

)

Find point P(I

min

)

Divide Trajectory (T) at P(I

min

)

T-before = T (0 to P(I

min

));

T-after = T(P(I

min

) to len(T));

Connect T-before of the occlud(ed) player to T-

ter o

the occlud(in

) pla

Algorithm 1: Trajectory Correction algorithm.

neither the first nor the last point, is chosen as a pivot

point. Two vectors (V1 and V2) are then calculated

from the pivot point to a point prior to P on both sides

(e.g., P(i-1) and P(i+1) in Figure 8). The angle (θ)

between the two vectors V1 and V2 is calculated

using the dot product formula in Equation 1:



𝑉



⨀𝑉





𝑉



.|

𝑉



(1)

Figure 8: Geometrical Trajectory Analysis. Player moving

left to right, where P(i) is the pivot point on the re-sampled

trajectory.

At each point P

, the angle θ



is calculated and

stored in an array of angles. The index of the minimal

angle in the array corresponds to the index of the point

where the breakpoint occurs, marking the moment

of occlusion. We then divide the trajectory at this

point into two trajectories (T

before

& T

after

). The

icSPORTS 2023 - 11th International Conference on Sport Sciences Research and Technology Support

198

process of finding the breakpoints is applied to the

trajectories of both the occluded and occluding

players. Trajectory correction is then applied by re-

connecting the trajectories such that T

before

of the

occluded player is connected to T

after

, of the occluding

player and vice versa (Figure 9). Corrected

trajectories are then assigned to the correct player ID

before the occlusion event occurrence.

Figure 9: Trajectory Correction Example. (Upper):

trajectories of two occluded players before correction.

(Lower): corrected trajectories using the proposed trajectory

correction algorithm.

3.5 Parameters Optimization

The performance of the trajectory correction

algorithm depends on three hyperparameters: The

Savitzky Golay filter window size (w), which

determines the number of data points used for curve

fitting, the polynomial order (n), which specifies the

order of the polynomial used to for fitting the data

within the window (w), and the step size (s), which

determines the interval at which the trajectory is

resampled for geometrical analysis and vector

calculations.

We divided our occlusion dataset into 80% (64

trajectories) for training and 20% (16 trajectories) for

testing. To select the best set of parameter values, we

performed a grid-search with 5-fold cross validation

on the training dataset. Our grid search was limited to

window sizes of [5,7,9], polynomial orders of [2,3,4],

and step sizes of [5,7,9,10] to expedite the results. It

is worth mentioning that those parameters were

constrained so that the polynomial order must be

smaller than the window size in order to fit the

polynomial using the data points within the window.

4 RESULTS AND DISCUSSION

The player detection and association steps were

executed on an Nvidia GTX 2080 GPU with 8 GB V-

RAM. The YOLO object detector was configured to

detect a single class, “Person”, and it successfully

detected the players in the scenes with 87% accuracy

during non-occlusion events as seen in Figure 8. The

detection accuracy is calculated by comparing the

number of detected persons in non-occlusion events

to a reference value of 23 persons, which represents

the expected number of individuals on the pitch (22

players+ 1 Referee). However, in cases where there

are failed detections in certain frames, these missed

detections are often detected in later consecutive

frames. Since the DAN does not rely on feeding

adjacent frames, it can effectively compensate for

these dropped detections during the association

process. The average runtime for the detection

algorithm is 0.25 second per frame to detect all

players.

On the other hand, the association step had an

error rate of 20%, which is calculated as number of

wrong player ID associations following an occlusion

event divided by the total number of occlusion events

in the game.

The occlusion detection algorithm detected 211

potential occlusion events in the game, refined to 40

events (80 trajectories) which were manually

confirmed. There is a noticeable difference between

the number suggested occlusion events and the

number of manually confirmed events because the

occlusion detection algorithm only considers the

Euclidean distance proximity between the detected

players. However, this proximity could occur when

two players get close for a limited number of frames.

In such cases, the DAN is capable enough to

overcome the occlusion and successfully associate

those players. Meanwhile, the occlusion detection

algorithm may still indicate this situation as a possible

occlusion due to spatial proximity.

The trajectory correction algorithm successfully

corrected 87.5% (70 trajectories) of the 80 disrupted

trajectories from the manually confirmed occlusion

events. The values of the hyperparameters (w, n and

s) used for the trajectory correction algorithm were

chosen through majority voting among the values that

yielded the highest validation accuracy (Table 2), as

described in section 3.5. The majority voting process

resulted in choosing a window of length 7, a

polynomial order of 2, and a step size of 7. The

average validation accuracy recorded was 90.5% on

the training data.

FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction

199

Table 2: Cross Validation Results.

Fold # s w n Validation Accuracy

0 7 7 2 0.923

1 9 5 2 0.846

2 7 7 2 1.0

3 7 7 2 0.923

4 9 5 4 0.833

The 10 discontinued trajectories are corrected

manually to generate complete and usable low-cost,

contactless player tracking data. The complete

tracking data can be visualized on 2D maps as shown

in Figure 10 and used to plot player movements as in

heatmap in the Figure 11.

Figure 10: A 2D visualization of the generated tracking data.

Overall, our proposed system is capable of providing

a semi-automated tool to collect tracking data through

entire game using the recorded match feed. To the

best of our knowledge, FOOTBALLTrace is the first

AI-based system to produce complete tracking data

that can be used in lieu of the GPS modules.

5 LIMITATIONS AND FUTURE

WORK

Although FOOTBALLTrace is an important step

towards the practical application of AI and Computer

vision-based tracking systems, manual interventions

are still required to generate usable AI-based football

tracking data. To develop fully automated and

accurate systems, more domain specific training

datasets are need. In this study, we were limited by

the lack of publicly available football tracking data.

Therefore, we utilized an object association network

(i.e., DAN) that is trained on pedestrian tracking data.

It is worth noting that pedestrian tracking data exhibit

less similarity between tracked objects, whereas

football tracking data exhibit a higher degree of

similarity between players, leading to more

unresolved occlusion events.

Figure 11: An example player movement over a specific

game segment of 5 min.

As a future solution, we plan to use

FOOTBALLTrace with minimal human

interventions to construct labeled football tracking

data and utilize transfer learning to improve the

performance of the DAN while reducing the number

of human interventions. Furthermore, we intend to

investigate the impact of increasing the track memory

parameter δ

on association performance during long-

term occlusions. Additionally, we will explore

optimization techniques such as grid search, coupled

with other types of distance metrics (i.e. Manhalobi’s

distance), to identify the optimal proximity metric

and threshold (Ʈ) for improving performance of the

occlusion detection algorithm.

Finally, we aim at reducing the runtime of our

system to generate a real-time tracking data.

Currently, the runtime of FOOTBALLTrace on a full

game (90 minutes) is approximately one day to

generate complete tracking trajectories. While real-

time tracking data collection may not be essential for

tactical and performance analysis, we plan to enhance

the runtime by implementing the entire system using

C++ and efficient GPU programming.

ACKNOWLEDGEMENTS

We would like to express our gratitude to Salma

Hazem, Doaa El Sherif, and Mohamed El-Badry at

KoraStats, Egypt, for providing us with the necessary

facilities and access to the video recordings of

football games, and for their efforts in implementing

the image stitching algorithm to create the panoramic

view data.

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