[Project] Football Players Tracking with YOLOv5 + ByteTRACK

This page summarizes the projects mentioned and recommended in the original post on /r/MachineLearning

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  • narya

    The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository contains the implementation of the flowing paper https://arxiv.org/abs/2101.05388. We also make available all of our pretrained agents, and the datasets we used as well.

  • Love this work! By chance, have you heard of the Narya API? They seem to be doing something similar where they're able to track players on the video feed and took it one step further by evaluating players based on "expected discount goals" using a Google Football reinforcement agent.

  • make-sense

    Free to use online tool for labelling photos. https://makesense.ai

  • Two things that carried me the most are my blog https://medium.com/@skalskip - which gave me my first job in computer vision, and my open-source GitHub project: https://github.com/SkalskiP/make-sense - which gave me all my jobs since I created it.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • notebooks

    Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

  • So, I published all the code here: https://github.com/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-football-players.ipynb And you can test the model online here: https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc/model/2

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