NBA-Player-Detector Object Detection Dataset and Pre-Trained Model by Francisco Zenteno

Images

366 images

Explore Dataset

Here are a few use cases for this project:

Real-Time Game Analysis: The NBA-Player-Detector can be used by coaches or analysts to track player movements, interactions between players, and ball possession in real-time. This could provide valuable insights for decision-making during games and fine-tuning of strategies.
Enhanced Sports Broadcasting: Broadcast companies can use the model to automatically detect and highlight players on the screen during a live broadcast. It can help viewers follow the game more closely, especially in identifying less known players, and enhance the overall viewing experience.
Player Training and Evaluation: The NBA Player Detector can be used to analyze the performance of individual players during training sessions or competitive games. It could help trainers identify areas where a player could use improvement, such as shooting or passing skills.
Sports Betting and Predictions: Bettors or prediction companies can use real-time or historical data from the model to predict player or team performance. Such insights may influence betting odds or decision-making in fantasy sports.
Fan Engagement and Interaction: Sports apps can integrate the computer vision model for interactive features, such as allowing fans to click on a player during a live game stream to view their statistics or history. This could significantly enhance fan engagement and satisfaction.

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{
                            nba-player-detector_dataset,
                            title = { NBA-Player-Detector Dataset },
                            type = { Open Source Dataset },
                            author = { Francisco Zenteno },
                            howpublished = { \url{ https://universe.roboflow.com/francisco-zenteno-uryfd/nba-player-detector } },
                            url = { https://universe.roboflow.com/francisco-zenteno-uryfd/nba-player-detector },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-04-30 },
                            }