Augmenting Vision-Based Human Pose Estimation with Rotation Matrix

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...

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Bibliographic Details
Published in:arXiv.org 2023-10
Main Authors: Vazan, Milad, Fatemeh Sadat Masoumi, Ou, Ruizhi, Rawassizadeh, Reza
Format: Article
Language:English
Subjects:
Activity recognition
Algorithms
Classification
Data augmentation
Optimization
Pose estimation
Rotation
Online Access:Get full text
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Summary: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%.
ISSN:2331-8422