# Voltra- volleyballfeedbackapp

## Project Overview  
This project detects volleyballs in video footage by combining a YOLO-based object detection model with custom color filtering techniques. The goal is to accurately identify and track the volleyball during gameplay, which can be used for game analysis, training, or automated highlights.

## Features  
- Utilizes a YOLO model pretrained on object detection tasks to identify potential volleyball locations.  
- Applies color filtering to refine detections based on the volleyball’s distinctive color.  
- Outputs bounding boxes around detected volleyballs in video frames.  
- Supports input video files in common formats (e.g., .MOV, .MP4).  

## Requirements  
- Python 3.8 or higher  
- OpenCV (`opencv-python`)  
- PyTorch (or relevant YOLO framework you are using)  
- NumPy  

## Future Improvements

Add pose estimation to analyze player movements alongside ball tracking.
Upgrade to create a feedback app that gives feedback to volleyball serve videos as well as other plays such as sets, spikes, etc using ball speed, accuracy, control. 

Enhance detection robustness under varied lighting and background conditions.

Implement real-time processing capabilities for live game scenarios.

Incorporate tracking algorithms to maintain volleyball identity across frames.