Track and count squats using your webcam
Detect and visualize poses in videos
Transform pose in an image using another image
Detect and label poses in real-time video
Synthpose Markerless MoCap VitPose
Analyze images and videos to detect and visualize human poses
Small Space to test ViTPose
Detect 3D object poses in images
Using our method, given a support image and skeleton we can
Estimate human poses in images
This app is used for Human pose Detection
Track body poses using a webcam
Visualize pose-format components and points.
Streamlit Webrtc Example is a web application built using Streamlit, designed to demonstrate real-time video processing capabilities. It integrates WebRTC (Web Real-Time Communication) to access and process video streams directly from the user's webcam. This example is particularly focused on pose estimation, enabling applications such as squat tracking and counting. It provides a user-friendly interface for users to interact with their webcam feed while leveraging AI to analyze movements in real time.
• Real-time Video Processing: Access and analyze live video feeds from your webcam.
• Pose Estimation: Detect and track human poses in real time using AI models.
• Squat Counting: Automatically count squats based on detected movements.
• Webcam Integration: Seamlessly connect to your device's camera for input.
• Data Visualization: Display movement data and squat counts on the interface.
• User-Friendly Interface: Easy-to-use controls for starting and stopping the tracking.
pip install streamlit opencv-python numpy
streamlit run app.py
http://localhost:8501).1. What is pose estimation, and how does it work?
Pose estimation is the ability to detect and track human body parts (e.g., joints and limbs) in images or video streams. In this app, AI models analyze the video feed to identify movements and count squats.
2. Can I use this app without a webcam?
No, the app requires a webcam to function, as it relies on real-time video input for pose estimation and squat tracking.
3. How accurate is the squat counting?
Accuracy depends on factors like lighting, camera quality, and movement clarity. For best results, ensure proper lighting and a clear view of your full body in the webcam frame.
4. Is my video data saved anywhere?
No, the app processes video data in real time and does not store it. All processing is done locally on your device.
5. Can I modify this example for other exercises?
Yes, you can customize the pose estimation model and tracking logic to detect other exercises like push-ups or lunges. This requires adjustments to the AI model and movement detection criteria.