DANCE POSE TRACKER
The Dance Pose Tracker is an interactive project that leverages the PoseNet machine learning model to
analyze dance movements in real-time from a video. By utilizing a pre-recorded video, this project tracks
and visualizes keypoints on the dancer's body, such as joints, to map out the
dance moves and display skeletal connections.
The program uses ml5.js and p5.js libraries to detect and plot these keypoints, creating a visual representation
of the dance poses. With an overlay effect, the project highlights specific body parts, allowing for a clear analysis of movement patterns. This application is ideal for tracking choreography, studying dance forms, or simply
experimenting with motion detection.
Users can press 'S' to save the visual output as an image, capturing a snapshot of any pose. This makes the
Dance Pose Tracker not only an interactive tool but also a creative way to visualize and understand the flow of
dance movements.
Situation
The need for a tool to analyze and visualize dance
movements in real-time led to the development
of the Dance Pose Tracker. This project was
designed to aid dancers, choreographers, and
dance enthusiasts in studying dance forms, tracking choreography, and exploring motion detection.
Task
The goal was to create an interactive program that
would track and map keypoints of a dancer’s body
from a pre-recorded video, displaying
skeletal connections and specific body part
movements in a way that could be saved and
analyzed later.
Action
I used PoseNet for body pose detection along with ml5.js and p5.js libraries to build a program that
tracks and plots keypoints on the dancer’s body,
such as joints, and visualizes the skeletal structure as the dancer moves. An overlay effect highlights
specific body parts, enhancing the clarity of
movement patterns. Additionally, I implemented a
feature that allows users to press 'S' to capture and save any frame of the dance pose as an image,
making it easy to review or study specific
movements.
Result
The Dance Pose Tracker successfully provided an
interactive and visually engaging way to map dance movements. It allowed users to capture dance poses and study movement patterns in detail, making it a valuable tool for anyone interested in dance analysis, choreography tracking, or experimenting with
real-time motion detection.
