{"id":13583873,"url":"https://github.com/SkalskiP/sports","last_synced_at":"2025-04-06T21:33:22.037Z","repository":{"id":64851354,"uuid":"578272278","full_name":"SkalskiP/sports","owner":"SkalskiP","description":"Cool experiments at the intersection of Computer Vision and Sports ⚽🏃","archived":false,"fork":false,"pushed_at":"2023-12-12T11:10:48.000Z","size":15840,"stargazers_count":482,"open_issues_count":1,"forks_count":33,"subscribers_count":12,"default_branch":"master","last_synced_at":"2024-10-30T11:02:40.552Z","etag":null,"topics":["computer-vision","deep-learning","deep-neural-networks","gpt-4","gpt-4-vision","object-detection","prompt-engineering","pytorch","sports-analytics","tutorial","yolov5","yolov7"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SkalskiP.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null},"funding":{"github":"SkalskiP"}},"created_at":"2022-12-14T16:58:14.000Z","updated_at":"2024-10-25T09:00:08.000Z","dependencies_parsed_at":"2023-12-12T11:54:44.719Z","dependency_job_id":null,"html_url":"https://github.com/SkalskiP/sports","commit_stats":null,"previous_names":["skalskip/sports","skalskip/sport"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SkalskiP%2Fsports","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SkalskiP%2Fsports/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SkalskiP%2Fsports/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SkalskiP%2Fsports/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SkalskiP","download_url":"https://codeload.github.com/SkalskiP/sports/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223264990,"owners_count":17116263,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","deep-learning","deep-neural-networks","gpt-4","gpt-4-vision","object-detection","prompt-engineering","pytorch","sports-analytics","tutorial","yolov5","yolov7"],"created_at":"2024-08-01T15:03:51.764Z","updated_at":"2024-11-06T00:30:54.157Z","avatar_url":"https://github.com/SkalskiP.png","language":"Jupyter Notebook","funding_links":["https://github.com/sponsors/SkalskiP"],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"# ⚽  Football Players Tracking with YOLOv5 + ByteTrack\n\n[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=QCG8QMhga9k)\n[![Roboflow](https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg)](https://blog.roboflow.com/track-football-players/)\n[![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-football-players.ipynb)\n\nI have long been fascinated by the use of Computer Vision in sports. After all, it is a combination of two things I love. Almost three years ago, I wrote a post on my personal blog in which I tried — at that time, still using YOLOv3 — to [detect and classify basketball players on the court](https://towardsdatascience.com/chess-rolls-or-basketball-lets-create-a-custom-object-detection-model-ef53028eac7d).\n\nFIFA World Cup 2022 has motivated me to revisit this idea. This time I used a combination of [YOLOv5](https://github.com/ultralytics/yolov5) and [ByteTrack](https://github.com/ifzhang/ByteTrack) to track football players on the field. This blog post accompanies the Roboflow video where I talk through how to track players on a football field.\n\nhttps://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4\n\n# 🤸  3D Football Players Pose Estimation with YOLOv7\n\n[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=AWjKfjDGiYE)\n[![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/SkalskiP/sport/tree/master/football-players-pose-estimation)\n\nI was watching a FIFA 2022 World Cup match the other day, and one of the things that caught my eye was VAR - Video Assistant Referee, or to be more precise, the part of it responsible for analyzing whether a player was on the offside. I did a little [research](https://www.youtube.com/watch?v=WycjDx6giVE) and found that the system performs pose estimation on multiple cameras at once. I decided to check how difficult it would be to reproduce it at home using two cameras and [YOLOv7](https://github.com/WongKinYiu/yolov7).\n\nhttps://user-images.githubusercontent.com/26109316/207677038-20f951a6-e469-4b3f-a934-66e036fcff69.mp4\n\n# 👕 Assigning Football Players to Teams by Uniform Color with GPT-4V\n\n[![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/SkalskiP/sport/tree/master/football-analysis-with-gpt4-vision)\n\nThis project explores the use of [GPT-4V](https://openai.com/research/gpt-4v-system-card) in sports analytics, specifically in the context of football. Its primary aim was to evaluate whether GPT-4V could effectively distinguish and assign players to teams based on the color of their uniforms. This was achieved through the implementation of several advanced vision prompting techniques.\n\nhttps://github.com/SkalskiP/sports/assets/26109316/b354b38d-1a12-477d-9283-45059ce12467\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSkalskiP%2Fsports","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSkalskiP%2Fsports","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSkalskiP%2Fsports/lists"}