Computer Vision

1. Image Acquisition::

Gathering a diverse dataset of images containing volleyball scenes within stadium settings. This may include different angles, distances, lighting conditions, and crowd densities.

2. Data Preprocessing:

Cleaning and annotating the collected images to prepare them for training. This involves tasks such as resizing, cropping, and labelling the volleyball within each image.

3.Model Selection:

Exploring and selecting suitable computer vision models for volleyball detection. This involve pre-trained models like YOLO (You Only Look Once).

4. Training:

Training the selected model(s) on the annotated dataset to learn the features and characteristics of volleyballs in stadium scenes. This phase involves fine-tuning the model parameters to optimise performance.

5. .Testing and Evaluation:

Assessing the trained model(s) using a separate test dataset to measure their accuracy, precision, recall, and other relevant metrics. This step helps identify any shortcomings and refine the model further.

6.Integration:

Integrating the trained model(s) into a practical application or system capable of real-time volleyball detection within stadium environments.

The computer accurately and swiftly detected volleyballs in diverse conditions, paving the way for real-world applications in volleyball matches and broadcasts