Sports Video Research Articles

Optimizing Football Formation Analysis via LSTM-Based Event Detection

The process of manually annotating sports footage is a demanding one. In American football alone, coaches spend thousands of hours reviewing and analyzing videos each season. We aim to automate this process by developing a system that generates comprehensive statistical reports from full-length football game videos. Having previously demonstrated the proof of concept for our system, here, we present optimizations to our preprocessing techniques along with an inventive method for multi-person event detection in sports videos. Employing a long short-term memory (LSTM)-based architecture to detect the snap in American football, we achieve an outstanding LSI (Levenshtein similarity index) of 0.9445, suggesting a normalized difference of less than 0.06 between predictions and ground truth labels. We also illustrate the utility of snap detection as a means of identifying the offensive players’ assuming of formation. Our results exhibit not only the success of our unique approach and underlying optimizations but also the potential for continued robustness as we pursue the development of our remaining system components.

A Comprehensive Review on Text Detection and Recognition in Scene Images

Detecting and recognizing text in natural scene images and videos is vital for several real-world applications, such as in the analysis of Crime scene CCTV footage, sports videos, and autonomous driving, to name a few. Therefore, one can expect several challenges, namely, arbitrarily oriented and shaped Text detection and identification in movies and natural environments. Many methods have been developed in the past to address these challenges, including advanced deep-learning models and transformers. Due to several methods available in the literature, it is not so easy to understand the open challenges, applications, directions, scope, limitations, and weaknesses of the methods. Therefore, there is a need to write a survey/review to highlight and discuss the strengths and weaknesses of the developed methods. This survey/review presents different categories of work and discusses their importance, limitations, new challenges, applications, and, finally, directions such that readers can choose appropriate methods and directions to carry out research work in the field of text detection/recognition in the natural scene and videos.

Retraction Note: Image optical processing based on convolutional neural networks in sports video recognition simulation

Retraction Note: Image optical processing based on convolutional neural networks in sports video recognition simulation

Prospective expertise: The use of ‘listen’ in the discourse of television sports pundits

This article details the functions of the imperative verb ‘listen’ in the pre-match build-up discussions of television football pundits. Data were recorded from live coverage of recent national and international football competitions. The analysis shows how ‘listen’ plays a role in what is referred to as prospective expertise: the work accomplished during the extensive pre-game build-up sections of a show, in which pundits engage in highlighting potential points of drama or sporting adversity to be anticipated in the game that is yet to be played. Three patterns are identified, in which different sequential positionings of ‘listen’ yield differing kinds of statements or evaluations, assessments, predictions and caveats regarding the forthcoming game.

A Face Recognition Method for Sports Video Based on Feature Fusion and Residual Recurrent Neural Network

A Face Recognition Method for Sports Video Based on Feature Fusion and Residual Recurrent Neural Network

AI-Based Cropping of Sport Videos Using SmartCrop

In the rapidly evolving landscape of digital platforms, the need for optimizing media representations to cater to various aspect ratios is palpable. In this paper, we pioneer an approach that utilizes object detection, scene detection, outlier detection, and interpolation for smart cropping. Using soccer as a case study, our primary goal is to capture the frame salience using object (player and ball) detection and tracking using AI models. To improve the object detection and tracking, we rely on scene understanding and explore various outlier detection and interpolation techniques. Our pipeline, called SmartCrop, is efficient, and supports various configurations for object tracking, interpolation, and outlier detection to find the best point-of-interest to be used as the cropping center of the video frame. An objective evaluation of the performance of individual pipeline components has validated our proposed architecture. Moreover, a crowdsourced subjective user study, assessing the alternative approaches for cropping from 16:9 to 1:1 and 9:16 aspect ratios, confirms that our proposed approach increases the end-user quality of experience.

Application of Interactive Object Model in Sports Teaching

Traditional physical education is just repeated training without quantitative teaching and training data results. Multimedia information analysis and retrieval technology can make repeated research on the content of competition or training, and increase the quantitative analysis of training indicators. Therefore, this paper proposes an interactive compressed domain key video object model to realize the design and production of physical education video, so as to quantify the effect of physical education. This paper discusses the interactive design and application of PE teaching video from three aspects: static, dynamic, and semantic. The interactive object model proposed in this paper can formulate corresponding strategies for different key sports video objects. The distortion rate of the complete key video object is 2.3%, which is 0.09% lower than the distortion rate of the Kim2002 scheme. From a usage perspective, through the scheme proposed in this article, coaches can obtain teaching videos with simple initial extreme values on an interactive interface, and the distortion rate of the video object is lower than that of traditional schemes.

ChatMatch: Exploring the potential of hybrid vision–language deep learning approach for the intelligent analysis and inference of racket sports

Video understanding technology has become increasingly important in various disciplines, yet current approaches have primarily focused on lower comprehension level of video content, posing challenges for providing comprehensive and professional insights at a higher comprehension level. Video analysis plays a crucial role in athlete training and strategy development in racket sports. This study aims to demonstrate an innovative and higher-level video comprehension framework (ChatMatch), which integrates computer vision technologies with the cutting-edge large language models (LLM) to enable intelligent analysis and inference of racket sports videos. To examine the feasibility of this framework, we deployed a prototype of ChatMatch in the badminton in this study. A vision-based encoder was first proposed to extract the meta-features included the locations, actions, gestures, and action results of players in each frame of racket match videos, followed by a rule-based decoding method to transform the extracted information in both structured knowledge and unstructured knowledge. A set of LLM-based agents included namely task identifier, coach agent, statistician agent, and video manager, was developed through a prompt engineering and driven by an automated mechanism. The automatic collaborative interaction among the agents enabled the provision of a comprehensive response to professional inquiries from users. The validation findings showed that our vision models had excellent performances in meta-feature extraction, achieving a location identification accuracy of 0.991, an action recognition accuracy of 0.902, and a gesture recognition accuracy of 0.950. Additionally, a total of 100 questions were gathered from four proficient badminton players and one coach to evaluate the performance of the LLM-based agents, and the outcomes obtained from ChatMatch exhibited commendable results across general inquiries, statistical queries, and video retrieval tasks. These findings highlight the potential of using this approach that can offer valuable insights for athletes and coaches while significantly improve the efficiency of sports video analysis.

Volleyball training video classification description using the BiLSTM fusion attention mechanism

This study aims to explore methods for classifying and describing volleyball training videos using deep learning techniques. By developing an innovative model that integrates Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanisms, referred to BiLSTM-Multimodal Attention Fusion Temporal Classification (BiLSTM-MAFTC), the study enhances the accuracy and efficiency of volleyball video content analysis. Initially, the model encodes features from various modalities into feature vectors, capturing different types of information such as positional and modal data. The BiLSTM network is then used to model multi-modal temporal information, while spatial and channel attention mechanisms are incorporated to form a dual-attention module. This module establishes correlations between different modality features, extracting valuable information from each modality and uncovering complementary information across modalities. Extensive experiments validate the method's effectiveness and state-of-the-art performance. Compared to conventional recurrent neural network algorithms, the model achieves recognition accuracies exceeding 95 % under Top-1 and Top-5 metrics for action recognition, with a recognition speed of 0.04 s per video. The study demonstrates that the model can effectively process and analyze multimodal temporal information, including athlete movements, positional relationships on the court, and ball trajectories. Consequently, precise classification and description of volleyball training videos are achieved. This advancement significantly enhances the efficiency of coaches and athletes in volleyball training and provides valuable insights for broader sports video analysis research.

Exploring global context and position-aware representation for group activity recognition

This paper explores the context and position information in the scene for group activity understanding. Firstly, previous group activity recognition methods strive to reason on individual features without considering the information in the scene. Besides correlations among actors, we argue that integrating the scene context simultaneously can afford us more useful and supplementary cues. Therefore, we propose a new network, termed Contextual Transformer Network (CTN), to incorporate global contextual information into individual representations. In addition, the position of individuals also plays a vital role in group activity understanding. Unlike previous methods that explore correlations among individuals semantically, we propose Clustered Position Embedding (CPE) to integrate the spatial structure of actors and produce position-aware representations. Experimental results on two widely used datasets for sports video and social activity (i.e., Volleyball and Collective Activity datasets) show that the proposed method outperforms state-of-the-art approaches. Especially, when using ResNet-18 as the backbone, our method achieves 93.6/93.9% MCA/MPCA on the Volleyball dataset and 95.4/96.3% MCA/MPCA on the Collective Activity dataset.

React: recognize every action everywhere all at once

In the realm of computer vision, Group Activity Recognition (GAR) plays a vital role, finding applications in sports video analysis, surveillance, and social scene understanding. This paper introduces Recognize Every Action Everywhere All At Once (REACT), a novel architecture designed to model complex contextual relationships within videos. REACT leverages advanced transformer-based models for encoding intricate contextual relationships, enhancing understanding of group dynamics. Integrated Vision-Language Encoding facilitates efficient capture of spatiotemporal interactions and multi-modal information, enabling comprehensive scene understanding. The model’s precise action localization refines joint understanding of text and video data, enabling precise bounding box retrieval and enhancing semantic links between textual descriptions and visual reality. Actor-Specific Fusion strikes a balance between actor-specific details and contextual information, improving model specificity and robustness in recognizing group activities. Experimental results demonstrate REACT’s superiority over state-of-the-art GAR approaches, achieving higher accuracy in recognizing and understanding group activities across diverse datasets. This work significantly advances group activity recognition, offering a robust framework for nuanced scene comprehension.

Examining User Recognition of a Sport-Centered OTT Service Using Topic Modeling and Sentiment Analysis Focusing on the SPOTV Now App

PURPOSE Recently, the need to improve service quality to provide an ideal viewing experience for sport OTT service users has increased. This study aimed to analyze user reviews of the SPOTV Now app, an OTT service specializing in sports in South Korea, to identify major perceptions of viewers and suggest measures for improvement.METHODS Review data (8,512) recorded on the SPOTV Now app were collected from November 21, 2017 to November 30, 2023. LDA-based topic modeling analysis was conducted along with a series of data preprocessing processes. Sentiment analysis was carried out through machine learning logistic regression analysis and odds ratio scores were calculated.RESULTS A total of five topics were derived, including ‘member management service', 'service stability', 'sport streaming service', 'sport video service', and 'subscription service'. Sentiment analysis indicated that 'exclusive', 'server', 'payment', 'log-in', 'authentication', 'pay', 'subscription', 'advertisement', 'play', and 'loading' were the top ten keywords.CONCLUSIONS Sport OTT operators should be more responsive to user requests and feedback, and video playback and server error problems should be improved for service stability. It is necessary to secure diversity as well as exclusivity of content, in addition to minimizing advertising or preparing various rate types, so that app users feel that they receive high value for their money.

Factors Influencing Willingness to Continue Using Online Sports Videos: Expansion Based on ECT and TPB Theoretical Models.

Digital sports, also known as online sports, are a new form of sport that users have tried in recent years. Despite the rapid growth of online sports, the factors influencing users' willingness to sustain their use are currently unknown. Based on the theory of planned behavior (TPB) and expectation-confirmation theory (ECT), this study empirically investigates the factors influencing the persistent use of online sports videos. Questionnaires were administered to participants. A total of 305 participants completed the questionnaire. Structural equation modeling showed that all hypotheses' paths were significant, except for H11 and H12. The results indicated that perceived usefulness, expectation confirmation, and coach social presence had a significant positive effect on users' satisfaction in using online sports videos. Moreover, satisfaction, behavioral attitudes, subjective norms, and perceived behavioral control had a significant positive effect on users' willingness to consistently use online sports videos, with gender moderating the impact of satisfaction and behavioral attitudes on the willingness to consistently use. We discuss the practical implications and recommendations for applying this study's findings.

“They’ve Never Played the Game”: “Cool Sports Girls,” Gender Inequality, and Garbage Time in Sports Punditry

In the 2010s, certain women in the sports television industry ascended beyond the often-reductive roles of studio hosts or sideline reporters, giving their sports opinions and occasionally hosting their own programs. This article argues that women who achieve this standing are forced to play a new role, that of the “cool sports girl.” Although “cool sports girls” gain male support through adopting masculine-coded traits, such as sports knowledge and smack talk, this article examines the misogynistic backlash these women face, interpreting the backlash as representing finite boundaries of the gains that women have achieved in a hypermasculine industry. This article reads the tenure of Katie Nolan at Fox Sports (2013–2017) as a representative example of how female television pundits exhibit agency amid entrenched industrial patriarchy.

Big data analytics for image processing and computer vision technologies in sports health management.

Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics. This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision. The fitness and health of professional athletes are analyzed using information from a publiclyavailable sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data. The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos. The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

Automatic Analysis and Event Detection Technology of Sports Competition Video Based on Deep Learning

This paper presents an investigation into the application of advanced techniques, including deep learning classification and FCM centroid segmentation, for automated event detection and analysis in sports videos. Through a series of experiments and analyses, we demonstrate the effectiveness of deep learning models in accurately categorizing events within sports footage, alongside the insights provided by FCM segmentation into event occurrences at the frame level. This paper investigates the application of advanced techniques, such as deep learning classification and FCM centroid segmentation, for automated event detection in sports videos. Through experiments, we achieved an average accuracy of 93.2% using deep learning classification and identified key events with probabilities ranging from 0.01 to 0.90 using FCM segmentation.

Video Analysis of Physical Education Teaching and Design of Intelligent Assistant Teaching System: Based on Motion Recognition and Optimization Algorithm

Nowadays, science and technology has rapidly increasing due to the specific requirements of education. The main aim of this technique is to enhance the physical education activities and helps sports man to finest teaching facilities of these activities. Moreover, the body changes with sports activities based on Deep Learning (DL) frameworks and classifier technology are examined to promote the practical application. Therefore, this paper Deep Reinforcement Learning based Chimp optimization (DRLbCO) algorithm was developed to improve the teaching efficiency of physical education system through the intelligent computer aided assessment models. Also, this model improving the performance, enhancing coaching effectiveness, and gaining competitive advantages in sports. Consequently, the proposed system is based on DL based image detection model DRL, which can analyse and provides proper actions in teaching students and enhance the ability of motion analysis in sports video training quality. Moreover, the DL model is integrated with optimization algorithm to optimize the performance. This could involve reinforcement learning, and other classifiers such as Support Vector Machine (SVM). Based on this provides feedback to students and teachers during physical education classes. The developed model is significantly enhances the physical education teaching student action in virtual sports instruction. Overall intention of the video assessment system of the sports training performance in analyzed.

A scoping review on physical literacy domains associated with participation in sports video games among youth and adolescence

A scoping review on physical literacy domains associated with participation in sports video games among youth and adolescence

Application of Sports Video Image Analysis Based on Fuzzy Support Vector Machine

Sports video image has always been a hot topic in sports video processing. The theoretical and experimental analysis of digital image noise reduction technology is a challenging topic. In this paper, a sports video denoising algorithm is designed by combining the excellent characteristics of curvilinear transformation theory and fuzzy support vector machine. Firstly, the image with noise is curvilinear, and the conversion coefficient is obtained. Then, according to the distribution characteristics of the system noise, the system parameters are divided into space, and the system learning features are constructed. The fuzzy classification of high-frequency curves is realized using the adaptive threshold denoising method. Then, the noise reduction coefficient is reconstructed by the curve-wave method to obtain the processed image. The simulation results show that this method can overcome the pseudo-Gibbs effect effectively and suppress the noise well. This algorithm has a good application prospect in sports video image processing.

Mimicry of European Football Commentary: Arap Uria’s Comic Lip-Sync Impressions in Kenyan Social Media

ABSTRACT The popularity of football in Africa is evident in the widespread viewership of televised top world league matches across the continent. Entertaining as they are to African fans, the broadcast images raise significant questions regarding the political implications of sports television as a medium for conveying cultural content. The matches are often played in far-off places and feature hardly any African commentators. New media technologies present African users with a host of technological affordances that enable them to challenge the hierarchical logic of industrial media production and the restricted communicative space of sports television. Comedy is a common form of political engagement with televised football images. This article analyses lip-sync impressions by a Kenyan comedian, Arap Uria, who records short parody videos that comment on iconic moments in football matches and posts them on social media platforms. Reading the audiovisual texts as politically motivated comic performances, this article argues that Uria’s lip-sync commentaries are a significant part of the contemporary sports television consumption culture in Kenya. The article discusses the place of the football commentator as a cultural mediator as well as how lip-sync performances can generate protest through artistic truth, remixing and parody.