Overview / Usage

The project demonstrates real-time pose estimation using the PoseNet model and ml5.js library. It aims to detect key body points from a webcam feed, allowing for creative overlays of images. This work showcases the potential of pose estimation technology in interactive applications, art installations, and augmented reality experiences. By solving the challenge of accurately tracking body poses, this project opens doors to various production applications, including fitness tracking, virtual try-on, and interactive gaming. The project's deployment on GitHub Pages makes it accessible for users to experience and experiment with pose estimation firsthand.

Methodology / Approach

The project employs the PoseNet model for real-time pose estimation, coupled with the ml5.js library to facilitate the integration. The methodology involves the following steps:

1.Webcam Capture: Utilizing the `createCapture()` function, the project captures live video from the user's webcam.

1. Pose Detection: The ml5.js library's PoseNet implementation is employed to detect body keypoints in the captured video frames. This is achieved by initializing the PoseNet model with the video feed.
2. Keypoint Visualization: The detected keypoints (e.g., nose, eyes, joints) are visualized using ellipses on the canvas. This gives users a clear view of the tracked body points.
3. Skeleton Visualization: The project also visualizes the skeletal connections between keypoints, providing a clear representation of body pose.
4. Image Overlays: By loading images like glasses and cigars, the project showcases creative overlay possibilities. These images are positioned based on specific keypoints' coordinates, enhancing user experience
5. Deployment: The project is deployed using GitHub Pages, enabling users to access and interact with the pose estimation functionality through a web browser.

Frameworks and Techniques:

• ml5.js: Leverages pre-trained machine learning models like PoseNet to simplify integration and implementation.
• HTML/CSS: Provides the webpage structure and styling for a user-friendly interface.
• Canvas Drawing: Utilizes HTML5 canvas for drawing video frames and keypoints.
• Image Loading: Loads overlay images using the `loadImage()` function.
• Real-Time Interaction: Offers an interactive experience by updating visualizations in real-time as users move.

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This methodology effectively merges pose estimation, creative visualization, and interactive technology to create an engaging and insightful project, showcasing the capabilities and potential applications of pose estimation technology.

Repository

https://yashz74.github.io/posenet-demo-ml5.js/