Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Goswami, Jai; Shoran, Preety; Shanbhog, Manjula Title A name given to the resource Real-Time Football Match Analysis with Region-Independent Player Tracking Using Deep Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Computer, Electronics, Electrical Engineering and their Applications, IC2E3 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC2E365635.2025.11167431" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC2E365635.2025.11167431</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018462669?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018462669?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Goswami J., CHRIST University, Computer Science, Bengaluru, India; Shoran P., CHRIST University, Computer Science, Bengaluru, India; Shanbhog M., CHRIST University, Computer Science, Bengaluru, India Description An account of the resource This project explores the application of AI in analyzing football games by tracking players across the entire video frame. Unlike traditional methods that focus on limited areas, the system here uses YOLO for detecting players everywhere in the frame and ByteTrack to follow them throughout the match. The goal is to get a clearer picture of each player's movement, particularly their speed and distance covered. Manual methods or GPS-based tools often fall short in providing quick, reliable data, especially in real-time scenarios. This study compensates for camera motion and adjusts for different viewpoints to get more accurate tracking results. As a way to test player identity consistency, the system randomly assigns popular player names to different tracking IDs. Experiments on public match videos show that the system can keep track of players even during zoom-ins, crowding, or partial visibility. Code snippets show how the model works in practice. Our results show that using full-frame AI tracking gives coaches more detailed tactical insights and helps them develop more effective strategies. 2025 IEEE. Subject The topic of the resource ball detection; ByteTrack; Frames; Full frame detection; Homography; Multi object tracking; Yolov8 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152439-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online