Augmenting Vision-Based Human Pose Estimation with Rotation Matrix
Milad Vazan Fatemeh Sadat Masoumi Ruizhi Ou Reza Rawassizadeh
Fitness applications are commonly used to monitor activities within the gym,
but they often fail to automatically track indoor activities inside the gym.
This study proposes a model that utilizes pose estimation combined with a novel
data augmentation method, i.e., rotation matrix. We aim to enhance the
classification accuracy of activity recognition based on pose estimation data.
Through our experiments, we experiment with different classification algorithms
along with image augmentation approaches. Our findings demonstrate that the SVM
with SGD optimization, using data augmentation with the Rotation Matrix, yields
the most accurate results, achieving a 96% accuracy rate in classifying five
physical activities. Conversely, without implementing the data augmentation
techniques, the baseline accuracy remains at a modest 64%.