oguchi-ebube Goto Github PK

action-recognition-soccer-players

A GUI which enables action classification and labelling of soccer player’s action in a broadcast video based on teams. Focus Areas: dribbling,kicking, running and passing using over 2000 sample images Methodology Create a flask application Connect to the User Interface(React) Use the trained model to process video frames Detect the skeleton(s) in each frame Label each detected skeleton(s) Give live analytics from the processing based on each team. Display the processed frames in a video format for the user. Get real-time action metrics for each team.

data-structures-algorithms-python

This tutorial playlist covers data structures and algorithms in python. Every tutorial has theory behind data structure or an algorithm, BIG O Complexity analysis and exercises that you can practice on.

deep-learning

flaskwithmongodb

A Sample Python Flask Application with MongoDBs

hello-world

just a random repository

hello-world-

none

new-file

form validation

oguchi-ebube.github.io

player_tracker

Tracking and finding coordinates for players in a youtube video

realtime-action-recognition_2

For Soccer player action detection

search-bing

soccer-player-action-recognition

A GUI which enables action classification and labelling of soccer player’s action in a broadcast video based on teams. Focus Areas: dribbling,kicking, running and passing using over 2000 sample images Methodology Create a flask application Connect to the User Interface(React) Use the trained model to process video frames Detect the skeleton(s) in each frame Label each detected skeleton(s) Give live analytics from the processing based on each team. Display the processed frames in a video format for the user. Get real-time action metrics for each team.

soccer-player-action-recognition-application

A GUI which enables action classification and labelling of soccer player’s action in a broadcast video based on teams. Focus Areas: dribbling,kicking, running and passing using over 2000 sample images Methodology Create a flask application Connect to the User Interface(React) Use the trained model to process video frames Detect the skeleton(s) in each frame Label each detected skeleton(s) Give live analytics from the processing based on each team. Display the processed frames in a video format for the user. Get real-time action metrics for each team.

soccer-players-detection-detectron2

This script is used for object detection and tracking of soccer players with background color segmentation of the soccer pitch using OpenCV in Detectron2. It is still a work in progress and the end goal is to get a model that can fully classify players on the field according to their teams with this process.

sugar-instance-packager

A library for packaging Sugar instances

video_meta_data_extractor

This file is used to extract metadata from a video file using exiftool

web-scrapper

It is used to scrape product reviews from jumia website

yolov5