Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA's 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing and its importance in video analytics and propose a future research perspective.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931798 | PMC |
| http://dx.doi.org/10.1109/SAM53842.2022.9827827 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
- a large-scale dataset of 3D medical shapes for computer vision.
Biomed Tech (Berl)
December 2024
Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany.
Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).
View Article and Find Full Text PDFDeep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall.
J Imaging
November 2024
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.
In recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow parameters from videos captured during snowfall conditions has become imperative for numerous future applications. This paper proposes a new analytical framework designed to extract traffic flow parameters from traffic flow videos recorded under snowfall conditions.
View Article and Find Full Text PDFHarnessing multimodal approaches for depression detection using large language models and facial expressions.
Npj Ment Health Res
December 2024
Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, 91052, Germany.
Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model.
View Article and Find Full Text PDFPulling at All Threads: Reflections on Using Multimodal Critical Discourse Analysis Within Arts-Based Health Research.
Qual Health Res
December 2024
École de Santé Publique de l'Université de Montréal and Centre de Recherche en Santé Publique, Montreal, QC, Canada.
Multimodal critical discourse analysis is a dynamic approach to qualitative data analysis that expands critical discourse analysis to include multiple communicative modes-such as images, graphics, video, and sound/music-into the semiotic analysis of ideology and power relations within contemporary forms of communication. We reflect on the potential of multimodal critical discourse analysis to be combined with arts-based health research as an analytic method to deconstruct discourses that shape the health and well-being of marginalized communities. Specifically, we frame this potential within our research about men's body image based a project using cellphilming and the deconstruction of cis-heteronormative and related ideologies.
View Article and Find Full Text PDFBeing vulnerable with viewers: Exploring how medical YouTubers communicated about COVID-19 with the public.
PLoS One
December 2024
Sporecyte, Orem, Utah, United States of America.
This study explores COVID-19 communication between medical experts who upload YouTube videos related to health/medicine (hereinafter medical YouTubers) and their viewers. We investigated three specific elements: (1) how medical YouTubers' use of words related to analytical thinking is associated with their viewers' engagement, (2) how medical YouTubers' use of different types of emotion is associated with their viewers' engagement, and (3) the emotional alignment between medical YouTubers and their viewers. We collected 194 COVID-related video transcripts from five YouTube channels and 375,284 comments from those videos.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!