Udemy - Real-Time AI Fitness Counter with Python and Computer Vision

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Udemy - Real-Time AI Fitness Counter with Python and Computer Vision (Size: 616.9 MB)
  1 -Calculating Angles in Pose Estimation.mp4 20.5 MB
  1 -Code Execution Workflow.mp4 97 MB
  1 -Course Introduction and Features.mp4 31.2 MB
  1 -Course Wrap-Up.mp4 9.1 MB
  1 -Human Fitness Tracking System Project Overview.mp4 22.7 MB
  1 -Installing Python.mp4 15.2 MB
  1 -Logic Behind Repetition Counting.mp4 127.8 MB
  1 -Model Inference and Code Explanation.mp4 116.6 MB
  1 -Package Installation Guide.mp4 24.8 MB
  1 -Packages Overview & MediaPipe Initialization.mp4 30.2 MB
  1 -Tkinter Implementation for UI.mp4 46.2 MB
  1 -Tkinter Log Window & Variable Initialization.mp4 30.1 MB
  2 -VS Code Setup for Python Development.mp4 19.2 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  Pushups.mp4 4 MB
  chest fly machine.mp4 2.4 MB
  dumbbell workout.mp4 12.4 MB
  fitness_tracking_final.py 16.4 KB
  requirements.txt 0 B
  squat.mp4 7.6 MB
  ▲ 21 total files

Description


Real-Time AI Fitness Counter with Python & Computer Vision

https://WebToolTip.com

Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 46m | Size: 616 MB

Smart Fitness: Real-Time Exercise Counting with AI using python and Computer Vision

What you'll learn
Understand the fundamentals of AI-based exercise tracking and its significance in real-time fitness monitoring.
Set up a Python development environment using Tkinter for UI and MediaPipe for pose estimation.
Implement real-time exercise counting for squats, push-ups, chest flys, and dumbbell lifts using MediaPipe.
Process live video feeds or uploaded videos to count exercises and provide feedback to users.
Learn pose detection techniques and how to apply them to analyze human motion accurately.
Develop a user-friendly interface with Tkinter to visualize exercise counts and provide real-time updates.
Optimize the system for accuracy and real-time performance in tracking and counting exercises.
Tackle challenges such as occlusions, variations in body posture, and different camera angles.
Explore potential applications in fitness training, rehabilitation, and personal workout tracking.

Requirements
Basic understanding of Python programming (recommended but not mandatory).
A laptop or desktop computer with internet access (Windows OS with a minimum of 4GB RAM).
No prior knowledge of AI or Machine Learning is required—this project is beginner-friendly.
Enthusiasm to learn and build practical AI-driven fitness applications.

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