Players_Detection Object Detection Dataset and Pre-Trained Model by WISD

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Explore Dataset

Here are a few use cases for this project:

Sports Analytics: This model can be employed by sports analysts, coaches, and teams to analyze player strategies, referee decisions, and game dynamics in real-time or post-match. It can provide insights into player-to-player interactions, ball possession ratios, goalkeeper's positions, and referee's involvement.
Game Highlights Generation: Media companies can use "Players_Detection" for automatic extraction of significant moments from a match, such as when a goal is scored, crucial saves by the goalkeeper, controversial referee decisions, and more. This can greatly help in creating high-quality match highlights efficiently.
Player Performance Assessment: Soccer training institutions may use this model for assessing individual player's performance during training or real matches by keeping track of the player's movements, interactions with the ball, and responses to the referee's decisions.
Augmented Reality Applications: For AR gaming apps based on real football matches, the model can be used to detect player, ball, goalkeeper, and referee positions, actions, and interactions accurately, providing real-time data to augment into the gaming software for an immersive experience.
Security and Crowd Management: Stadium security authorities can adopt the model to monitor player and referee behavior to ensure adherence to conduct codes. By identifying the players and their positions, it can help in avoiding or controlling pitch invasions or other security incidents.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            players_detection-g8i59_dataset,
                            title = { Players_Detection Dataset },
                            type = { Open Source Dataset },
                            author = { WISD },
                            howpublished = { \url{ https://universe.roboflow.com/wisd-jeelv/players_detection-g8i59 } },
                            url = { https://universe.roboflow.com/wisd-jeelv/players_detection-g8i59 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-06-26 },
                            }