Video Analysis Object Detection Dataset and Pre-Trained Model by abla

Images

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Explore Dataset

Here are a few use cases for this project:

Sports Analytics: The model can be used to track the movements of players, the ball, and the arbitrator in a variety of sports such as football, basketball, or rugby. This information could be beneficial to teams and individual players to play better strategically, enhance gameplay, and review referee decisions.
Broadcasting Applications: The model could help achieve better production quality in live sports broadcasts. By identifying and tracking the ball, players, and arbitrator, it can automate camera switching or improve replay highlights, enhancing the viewer experience.
Player Performance Evaluation: Coaches could use the model to assess individual player performance. By tracking the player’s movements along with the ball and arbitrator, it can expose more data points for assessing player effectiveness, agility, and decision-making.
Sports Betting: In the betting industry, the model can bring more accuracy in real-time analysis. The understanding of players' performances and ball possession in relation to the arbitrator’s decisions can hypothetically guide betting decisions.
Virtual Reality Training: VR game developers could use this model to create more realistic sports games or training simulations where amateur athletes or gamers can test strategies, gain insights, and improve their skills based on player-ball-arbitrator interactions.

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{
                            video-analysis-lzfo7_dataset,
                            title = { Video Analysis Dataset },
                            type = { Open Source Dataset },
                            author = { abla },
                            howpublished = { \url{ https://universe.roboflow.com/abla-qxhh0/video-analysis-lzfo7 } },
                            url = { https://universe.roboflow.com/abla-qxhh0/video-analysis-lzfo7 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-04-28 },
                            }