Week 6: Elevating Our Pose Detection Capabilities with Real-Time Processing

Hello and welcome back to my senior project blog! This week, we’ve transitioned our pose detection capabilities from static image processing to dynamic, real-time video analysis. Let’s explore the key changes in our approach and how they impact the project.

Transitioning to Real-Time Video Processing

Previously, our application focused on analyzing single images to detect human poses. While effective for static tests, this approach did not cater to the dynamic nature of physical exercises, where movement and continuity play crucial roles.

I have now adapted the program to handle a continuous video stream, accessing the device’s camera to capture real-time movement and change in posture as it happens. By processing each frame in real time, the app can provide instantaneous feedback on the user’s exercise form, making it more interactive and beneficial for on-the-spot corrections.

Simplification and Direct Processing

In our previous setup, the detection process involved converting pose landmarks into a protobuf format, and then rendering them onto the image. This method, while precise, introduced unnecessary complexity into our application. Now, I am employing the MediaPipe Solutions API to directly use the output from the pose detection to visualize landmarks on the video frames. This direct approach reduces the complexity of our code and enhances the system’s efficiency. By eliminating intermediary steps, we ensure that our application is faster and more responsive, crucial factors for real-time performance.

Real-Time Interaction Enhancements

Adding real-time video capture and display functionality has transformed our application from a static analyzer into a dynamic training tool. This development allows the app to interact with users, providing a more engaging and effective training experience. With the capability to process video frames in real time, our app is one step closer to being completed.

Looking Ahead

Moving forward, now that we have a working pose-detection program, I will begin to work on the grading system for the squat. Stay tuned for next week’s update, where I will delve into optimizing real-time processing capabilities and developing a form analysis algorithm. Thank you for following along on this exciting journey!