Football Video Research Articles

The superstar effect on perceived performance in professional football: An online experiment

We conduct a novel experiment to investigate whether football superstars consistently receive more favorable evaluations than non-superstars. Engaging 500 participants from Prolific, we randomly assign them to evaluate the same football videos with either visible or obscured players. In the control group, where players are visible, superstars receive lower performance ratings than non-superstars, challenging common perceptions. This trend is more intensified in the treatment group, where obscured identities result in even lower ratings for superstars, relative to non-superstars, suggesting a diminished superstar premium. These findings provide causal experimental evidence contributing to the literature on evaluation bias and the superstar effect.

The role of football video games in boosting cognitive abilities critical to football performance

Background and Aim: Football requires rapid decision-making and quick reaction times, crucial cognitive abilities that traditionally develop through physical training. This study explores whether football video games, which simulate real game scenarios, can enhance these cognitive abilities among college-level male football players. Materials and Methods: The study involved 51 football players from SRM Group of Institutions, divided into three groups: regular video game players (Group A), occasional players (Group B), and non-players (Group C). A mixed-methods approach was used, combining quantitative cognitive abilities tests (decision-making, reaction time, situational awareness). The video game used in the study was eFootball 2024 with participants engaging in gameplay over a period of 6 weeks. Tests were conducted before and after a set period of video game engagement. Data were analyzed using mixed-design ANOVA and Pearson’s correlation. Results: Significant improvements were observed in Group A across all cognitive abilities tested. Decision-making showed notable group and time effects (F (2, 48) = 5.76, p = 0.005, η² = 0.17; F (1, 48) = 12.54, p < 0.001, η² = 0.25), with interaction effects indicating substantial enhancements over time (F (2, 48) = 3.45, p = 0.035, η² = 0.08). Reaction time and situational awareness followed similar patterns, with significant group, time, and interaction effects. Correlation analysis revealed strong interrelations between the cognitive abilities, indicating that improvements in one area positively influenced others. Conclusion: Regular engagement with football video games significantly enhances cognitive abilities essential for football performance. These findings suggest that integrating video games into training programs could complement traditional methods, offering a valuable tool for cognitive development in sports.

Millennial football fan participation: the influence of football video games on play and engagement

Purpose Traditional sports have seen declining participation at many levels, with football being no different. This is occurring at a time when emergent technologies present new challenges, particularly to the crucial yet ignored cohort of millennials. Without meeting the needs of millennials, football cannot be successful in the future. This research seeks to understand how millennial football fandom (sport, not team) in Australia impacts football participation, whilst empirically examining the impact of football video games (FVGs).Design/methodology/approach Survey data are collected from online groups, forums and social media pages of Australian football (soccer) fans. Quantitative analysis of millennial fandom and its influence on football participation (for the first time demarcated into play and engagement) is undertaken, including the moderating influence of time spent playing FVGs, amidst covariate influences of age and number of children.Findings Results highlight the multi-dimensionality of millennial football fandom in Australia, reveal the typical hours spent playing football across a range of participation types (including play and engagement), support fan involvement’s influence on engagement with football, establish that a desire to interact with other football fans manifests in playing more football, specify how playing FVGs moderates these relationships, supports the covariate influences of age and evidences that playing FVGs does not hamper football play.Originality/value This is the first study to examine millennial fans of football (the sport, not tied to a club) and the influence of fandom on football participation. By separating football participation into two forms, play and engagement, we highlight discrete influences, whilst evaluating for the first time the moderating influence of the time millennials spend playing FVGs. For sport managers and administrators, these are important findings to facilitate better segmentation, recruitment, retention and participation, each with broader societal health benefits. This is undertaken in Australia where football is not a dominant code, relegating fandom to a niche, thus revealing important findings for sports and business management.

Football Video Image Restoration Based on Generalized Equalized Fuzzy C-mean Clustering Algorithm

Football Video Image Restoration Based on Generalized Equalized Fuzzy C-mean Clustering Algorithm

Detecting and Tracking Player in Football Videos Using Two-Stage Mask R-CNN Approach

Football is one of the most popular sports worldwide and capable of attracting the attention of millions of fans to a single match in the top leagues. The English Premier League, Spanish LaLiga, German Bundesliga, Italian Serie A, and French Ligue 1 are the five best leagues in the world today. There was an experiment where researchers want to analyze the efficiency and accuracy percentage of tracking and detection using the deep learning method of the Mask R-CNN model in classifying positive and negative X-Ray images in football matches. In this study, we applied Mask R-CNN for the segmentation and detection of football players. This model was based on two different backbones, namely ResNet101 and DenseNet. Both backbones produced accuracy values that were not significantly different, but the DenseNet approach performed better than ResNet101 based on testing results in the validation and testing sets. Based on comprehensive experiment results on the dataset, it has been shown that the Mask R-CNN approach with DenseNet can achieve better results compared to Mask R-CNN with ResNet101. Due to insufficient understanding of the characteristics of image types and the uneven distribution of various types of data sourced from random videos, there was still room for improvement in the trained model.

Automated player identification and indexing using two-stage deep learning network

American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crowded settings, distorted objects, and imbalanced data for identifying players, especially jersey numbers. In this work, we propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. First, we utilize an object detection network, a detection transformer, to tackle the player detection problem in a crowded context. Second, we identify players using jersey number recognition with a secondary convolutional neural network, then synchronize it with a game clock subsystem. Finally, the system outputs a complete log in a database for play indexing. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos. The proposed system shows great potential for implementation in and analysis of football broadcast video.

Analysis and Research on Technical and Tactical Action Recognition in Football Based on 3D Neural Network

Abstract The current action recognition analysis method is easily affected by factors such as background, illumination, and target angle, which not only has low accuracy, but also relies on prior knowledge. Research on the identification and analysis of technical and tactical movements in football. According to the characteristics of football video, a multi-resolution three-dimensional convolutional neural network is constructed by combining the convolutional neural network and the three-dimensional neural network. The supervised training algorithm is used to update the network weights and thresholds, and the video images are input into the input layer. After the convolutional layer, sub-sampling layer and fully connected layer and other network layers to obtain action recognition results. The principal component analysis method is used to reduce the dimension to process the action data set, and the Fourier transform method is used to filter out the principal component noise. The experimental results show that the method can effectively identify the technical and tactical movements of athletes from complex football game videos, and analyze the applied technical and tactical strategies. The average value of accuracy, recall and precision of technical and tactical analysis is as high as 0.96, 0.97, and 0.95, and the recognition and analysis effect has significant advantages.

Automatically Labeling Offensive Formations in American Football Film Using Deep Learning

Web services for storing, annotating, and sharing sports videos by high-school athletic teams have become more prevalent in recent years. However, most services lack the ability for coaches to automatically tag film, leading to many hours of manual annotation. For American football videos, coaches need to label formations, plays, and field positions in order to extract insights and create strategic game plans. This paper presents an end-to-end machine learning pipeline for automatically labeling American football offensive formations in videos. The pipeline includes pre-processing of videos, image classification, and a novel inference approach. The study compares a custom CNN model with pre-trained image classifier models using transfer learning. Specifically, CNN-based architectures (MobileNet, Inception, EfficientNet, etc.) and a transformer-based Vision Transformer (ViT) are compared. All models are trained on ~1400 images with the three most popular formation labels extracted from video clips of high-school football team games. The results show that several models, including the custom CNN model, achieved greater than 90% classification accuracy on the test dataset. The inference is performed by sampling multiple frames from a video clip, passing them through a trained image classifier, and taking a majority vote on the classification results to determine the final outcome. Our study found that using a sampling rate of 0.5 seconds, starting at 1 second, and taking five frames yields the highest inference accuracy of 95.4% using a trained customized CNN model. This system can assist all levels of football coaches in the analysis of game footage and formation identification.

A Beautiful Game: Interpreting Football Videogame Experiences

The purpose of this study is to explore football videogame playing experiences using a videogame esthetic that draws upon the MDA (mechanics–dynamics–esthetics) framework. In accordance with MDA, videogame esthetics comprises eight aspects—sensation, fantasy, narrative, challenge, fellowship, discovery, expression, and submission—derived from game-playing experiences. As a means of collecting and interpreting data, semi-structured interviews and qualitative thematic analysis were used. The study involved 30 interviewees who are both football players and football video game players. In view of the findings of the study, the concept of football videogame as sensual pleasure encompasses a number of attributes—game as make-believe, game as drama, game as a social framework, and game as self-discovery—because these attributes are valued in their relation to pleasure. Moreover, football videogames function as an uncharted medium in the field of football simulation due to their transcendence of time and space. For the esthetics of football videogames, the study proposes two major implications. It is essential to note that the esthetics of football videogames emphasize discovery and sensation, and playing football videogames is another way to consume football.

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Football is one of the most-watched sports, but analyzing players’ performance is currently difficult and labor intensive. Performance analysis is done manually, which means that someone must watch video recordings and then log each player’s performance. This includes the number of passes and shots taken by each player, the location of the action, and whether or not the play had a successful outcome. Due to the time-consuming nature of manual analyses, interest in automatic analysis tools is high despite the many interdependent phases involved, such as pitch segmentation, player and ball detection, assigning players to their teams, identifying individual players, activity recognition, etc. This paper proposes a system for developing an automatic video analysis tool for sports. The proposed system is the first to integrate multiple phases, such as segmenting the field, detecting the players and the ball, assigning players to their teams, and identifying players’ jersey numbers. In team assignment, this research employed unsupervised learning based on convolutional autoencoders (CAEs) to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the distance between those on opposing teams. This paper also created a highly accurate approach for the real-time detection of the ball. Furthermore, it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99. Since achieving a high performance in deep learning requires a large training set, and the collected dataset was not enough, this research utilized transfer learning (TL) to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy. To test the proposed system, this paper presents a comprehensive evaluation of its individual stages as well as of the system as a whole. All codes, datasets, and experiments are available on GitHub ().

Analysis of the optimal shooting angle in football matches based on network data mining

As an important research field in video understanding, human behavior recognition is widely used in intelligent monitoring, intelligent nursing, interactive behavior, and robot control. ObjectiveThe purpose of this study is to explore the analysis method of interaction behavior of soccer video. MethodsIn this study, we use scene simulation, big data analysis, experimental comparison, and other methods to analyze the interaction between players in a football video. The resultsof experiment and analysis of attack mode under interactive behavior show that the accuracy of attack direction determination results in traditional video analysis method and accurate analysis method is 75.60 % and 85.2 %, respectively. In this study, taking into account the two factors of staff proportion and average distance, the accuracy is improved to a certain extent, which can reach 91.5 %. It can be concluded that the deviation rate proposed in this study can better describe the position information of the football, can realize the detection of the basic attack mode, and can enable the computer to identify the attack direction of both players in the shortest time, which shows that the method is efficient and accurate. It provides a good analysis method for the research of human interaction behavior in football video and even in all kinds of videos.

‘White digital footballers can’t jump’: (re)constructions of race in FIFA 20

ABSTRACT Drawing on the quantitative datasets of Electronic Arts’ FIFA 20 top 100 players and their qualitative descriptors, the paper addresses the following: (1) How do ideas of race manifest and influence digital worlds? (2) How do digital football simulations (games) disrupt or reproduce racialised stereotypes and logics found within football in the social world? (3) How does playing football video games contribute to users’ understandings of race and sport. This paper, the first in-depth study of its kind, provides new empirical insights into the presence of the ‘natural athlete’ discourse within the operative datasets that underpin White and Black digital player performances in FIFA 20. We conclude that FIFA 20 is a site for a potent experiential socialisation in racialised myths, where gamers come to know race and the racialized other in sport through feeling the racialized differences of the procedurally generated natural athlete quite literally through their controllers.

Application of Intelligent Analysis Technology of Football Video Based on Online Target Tracking Algorithm of Motion Characteristics in Football Training.

Because the scene of football video is fixed and simple, the events in football video, such as shooting and offside , also have clear semantics. At the same time, they have sufficient domain knowledge and have broad application prospects. The research framework of sports video intelligence analysis is usually regarded as a three-level framework, namely, the low-level feature layer, the middle-level key primitive generation layer, and the high-level event analysis layer. A MF_O2T (Moving Feature Online Target Tracking) algorithm is proposed. First, based on the marked first frame image, this algorithm extracts a set of standardized local images from the target areas of visible and infrared images as target convolution filters, removes the main color of the stadium by using HSV color space nonuniform quantization algorithm, and extracts the histogram of the main color of players in upper and lower blocks. Experimental results show that the algorithm designed in this paper has strong robustness, can better adapt to player tracking in different scenes of football video, and meets the real-time requirements of football training.

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.

Deep Learning-Based Football Player Detection in Videos.

The main task of football video analysis is to detect and track players. In this work, we propose a deep convolutional neural network-based football video analysis algorithm. This algorithm aims to detect the football player in real time. First, five convolution blocks were used to extract a feature map of football players with different spatial resolution. Then, features from different levels are combined together with weighted parameters to improve detection accuracy and adapt the model to input images with various resolutions and qualities. Moreover, this algorithm can be extended to a framework for detecting players in any other sports. The experimental results assure the effectiveness of our algorithm.

Football Game Video Analysis Method with Deep Learning.

Football is a beloved sport, and its wide audience makes football video one of the most analytically valuable types of video. Researchers have achieved certain research results in football video content analysis. How to locate interesting event clips from a complete long video is an urgent issue to be addressed in football game video analysis. The granularity of sports event detection results with traditional machine learning is relatively coarse, and the types of events that can be detected are limited. In recent years, deep learning has made good progress in the research of video single-person events and action detection, but there are few achievements in the detection of sports video events. In response to this problem, this work uses a deep learning method to build an event detection model to detect events contained in football videos. The whole model is divided into two stages, in which the first stage is utilized to generate candidate event fragments. It divides the football video to be detected into a sequence of frames of a certain length and scans using a sliding window. Multiple frame sequences within a sliding window form a segment, and each segment is a prediction unit. The frame sequence features within the segment are obtained through a three-dimensional convolutional neural network, which is used as the input of each time point of the bidirectional recurrent neural network and further integrated to generate the event prediction of the segment. The second stage is to further process the above results to remove all segments predicted as nonevents. The thresholds are set according to the detection effect of various events to filter out event fragments with higher probability values, obtain the start and end positions of the events through merging, classify and mark them, and finally output complete event fragments. This work has carried out comprehensive and systematic experiments to verify correctness of the proposed method.

Visualization of Football Tactics with Deep Learning Models

In the last several years, computer vision tasks involving visual identification and tracking have seen a rise in the usage of deep learning technologies in recent years. An extremely difficult but rewarding endeavor is identifying and following football players’ targets. This may be used to study football tactical visualization. Due to the similar appearance and frequent occlusion of targets in football video, traditional methods often can only segment targets such as players and balls in the image but cannot track them or can only track them for a short time. Based on the related research of computer vision and deep learning, using several cameras, this study develops a system that can properly monitor many targets in a football stadium for a lengthy period of time. The main research contents of this paper are as follows: (1) a CNN for target displacement prediction is proposed, which no longer relies on the previous linear motion model or quadratic motion model, so that the multitarget tracking algorithm can be applied to more scenes. (2) For the first time in a multitarget tracking algorithm, a continuous conditional random field is used to model the asymmetric nature of the target relationship. At the same time, the CNN for target displacement prediction can be cascaded with the continuous conditional random field for end-to-end training, which greatly reduces the training difficulty. The parameters of the experiment in this paper are simple, and comprehensive and systematic experiments verify the validity and correctness of this work from different aspects.

IAUFD: A 100k images dataset for automatic football image/video analysis

Nowadays, analyzing football videos using computer vision techniques has attracted increasing attention. Significant events detection, football video summarization, football results predictions, statistics etc. are exciting applications in this area. On the other hand, the deep learning approaches are very successful methods for image and video analysis that need much data. Nevertheless, to the best of our knowledge, publicly available datasets in this area are small or individual, which are not enough for such deep learning-based approaches. A public dataset was collected, annotated, and prepared, namely IAUFD** Islamic Azad University Football Dataset (IAUFD) http://sites.google.com/view/image-and-video-analysis, to meet this gap for researches in this direction. The IAUFD contains 100,000 real-world images from 33 football videos in 2,508 min, annotated in 10 event categories. These categories include the goal, center of the field, celebration, red card, yellow card, the ball, stadium, the referee, penalty-kick, and free-kick. It is believed that these moments are the basis and useful for any high-level action or event exploration. For a generalization of our dataset, we paid attention to various weather (e.g., sunny, rainy, cloudy etc.), season, time of day, and location. We also used two deep neural networks (VggNet-13 and ResNet-18) to evaluate our proposed dataset as the baseline for future studies and comparison.

Automatic Detection Algorithm of Football Events in Videos.

The purpose is to effectively solve the problems of high time cost, low detection accuracy, and difficult standard training samples in video processing. Based on previous investigations, football game videos are taken as research objects, and their shots are segmented to extract the keyframes. The football game videos are divided into different semantic shots using the semantic annotation method. The key events and data in the football videos are analyzed and processed using a combination of artificial rules and a genetic algorithm. Finally, the performance of the proposed model is evaluated and analyzed by using concrete example videos as data sets. Results demonstrate that adding simple artificial rules based on the classic semantic annotation algorithms can save a lot of time and costs while ensuring accuracy. The target events can be extracted and located initially using a unique lens. The model constructed by the genetic algorithm can provide higher accuracy when the training samples are insufficient. The recall and precision of events using the text detection method can reach 96.62% and 98.81%, respectively. Therefore, the proposed model has high video recognition accuracy, which can provide certain research ideas and practical experience for extracting and processing affective information in subsequent videos.

Detection of Shot Transition in Sports Video Based on Associative Memory Neural Network

Users must quickly and effectively classify, browse, and retrieve videos due to the explosive growth of video data. A variety of shots make up the video data stream. The most important technology in video retrieval is shot detection, which can fundamentally solve many problems, resulting in improved detection effects and even directly affecting video retrieval performance. This paper investigates the shot transition detection algorithm in digital video live broadcasts based on sporting events. To solve the problem of shot transition detection using a single training sample, an AMNN (Associative Memory Neural Network) model with online learning ability is proposed. Experiments on a large football video data set show that this algorithm detects shear and gradual change better than existing algorithms and meets the application requirements of sports video retrieval in most cases.