computer vision analysis of football project to learn
- Use ultralytics and YOLOv8 to detect objects in images and videos.
- Fine tune and train your own YOLO on your own custom dataset.
- Use KMeans to cluster pixles and segment players from the background to get the t-shirt color accurately.
- Use optical flow to measure the camera movement.
- Use CV2's (opencv) perspective transformation to represent the scene's depth and perspective.
- Measure player's speed and distance covered in the image.
- additional detection options: keypoints for pose-detection, masks for segmentation
- converting video into frames, inference on individual frames into list detection object, creating tracker on list of detections
Summary of work
- Setting up folders and initializing the YOLO model for object detection
- Demonstration of AI/ML football analysis system using YOLO, OpenCV, and Python
- Understanding object detection and bounding boxes in AI/ML football analysis
- Improving detection accuracy and excluding non-relevant objects
- Utilizing Roboflow's football player detection dataset
- Setting up football data set for AI/ML analysis
- Moving data sets to specific folders for code reproducibility
- Training progress and downloading model weights
- Using YOLO, OpenCV, and Python to analyze football with AI/ML
- Setting up video reading and saving utilities with CV2 library
- Setting up output video format and writing frames to video writer
- Implementing object tracking for player analysis using bounding boxes
- Implementing object tracking using YOLO and a specific tracker
- Setting minimum confidence for object detection and addressing false detections
- Implementing object tracking with YOLO and OpenCV
- Using YOLO and OpenCV for object detection in football analysis
- Implementing class detection and verification in AI/ML Football Analysis system
- Tracking and extracting bounding boxes for players, referees, and ball in a football video
- Implementing object tracking for football analysis using YOLO and OpenCV
- Saving and loading data using pickle in Python
- Developing code to visualize the predictions using circles instead of bounding boxes
- Extracting center and width of bounding boxes for football analysis
- Drawing an ellipse using CV2 function
- Implementing AI/ML tracking for players and referees in football analysis
- Calculating X and Y positions for the rectangle center
- Implementing object tracking and drawing in AI/ML Football Analysis system
- Defining triangle points based on bounding box for AI/ML Football Analysis
- Developing a football analysis system with Python and OpenCV
- Implementing image processing and analysis in Python using YOLO and OpenCV
- Implementing K-Means clustering for image segmentation
- Determining player and non-player clusters using color analysis
- Implementing a clustering model using K-means algorithm
- Implementing K Means clustering for player color detection
- Implement player team identification using player ID and color
- Assign players to teams based on their colors
- Utilizing team colors for player tracking
- Using pandas to interpolate missing values for more complete detections
- Implementing ball tracking and player assignment using YOLO and OpenCV
- Creating a module for player ball assignment
- Assigning players to balls using AI algorithm
- Implementing tracking of players with assigned players and has ball attribute
- Drawing semi-transparent rectangles for football analysis
- Calculate the percentage of time each team has the ball
- Adjusting for camera motion to accurately measure player speed and distance
- Detecting corner features and camera movement using Optical flow
- Initializing parameters for feature extraction in AI/ML Football analysis
- Setting parameters for feature extraction and tracking in football analysis
- Implementing a function to measure distance and camera movement
- Implementing camera movement detection using YOLO and OpenCV
- Implementing camera movement tracking and displaying on the frame
- Implementing player positions robust to camera movement
- Adjusting positions according to camera movement
- Implementing camera movement estimator for position adjustment
- Discussing the football court dimensions and calculations
- Converting camera-adjusted position to real-world positions
- Implementing perspective transform and transforming points
- Implementing transform Point function for AI/ML Football Analysis system
- Creating a speed and distance estimator using Python
- Calculating speeds and distances for players using object tracking
- Calculating speed and distance in football analysis
- Implementing speed and distance calculations for object tracking in AI/ML football analysis system
- Implementing speed and distance estimator in the main program
- Building an AI/ML Football Analysis System with YOLO and OpenCV