This week, I researched existing boxing pose databases for use with MediaPipe. After some exploration, I found limited publicly available datasets specifically designed for boxing pose recognition. Given this, I started to explore the possibility of training my own boxing pose classifier.
To address the lack of existing data, I considered a systematic approach, starting with the creation of a custom dataset using MediaPipe’s Pose solution. This involves capturing videos of different boxing actions (e.g., jabs, hooks, uppercuts) using a webcam, extracting key pose landmarks, and annotating them. I wrote some preliminary code to collect data and extract landmarks. Following this, I planned to split the data into training, validation, and testing sets, and possibly train machine learning models such as SVMs or neural networks to classify boxing poses in real-time.
Additionally, I studied the MediaPipe Unity Plugin. I downloaded the plugin package and reviewed its documentation, which will help integrate with the gaming design.
My progross is on schedule, and for next week, I am aiming to start with the pose annotation and present some experimentation results gained by using the current method.