Thesis Details
Detekce jógových pozic v obraze
The motivation for this thesis is the concept of a smartphone app detecting Yoga poses and displaying Yoga frames to a user. The goal of this project is proving that even a simple Convolutional Neural Network (CNN) model can be trained to recognize and classify video frames from a Yoga session. I created an application in which the videos are manually annotated. The data, consisting of frames captured from 162 collected videos based on the annotations, is then passed to train a CNN model. The Dataset consists of 22 000 images of 22 different Yoga poses. The frames are captured using the OpenCV library, the training process is handled by the TensorFlow platform and the Keras API, and the results are visualized in the TensorBoard toolkit. The Model's multi-class classification accuracy reaches 91% when the binary cross-entropy loss function and the sigmoid activation function are used. Despite the promising experimental results, the main contributions are the dataset forming tools and the Dataset itself, which both helped to confirm the proof-of-concept.
Yoga Pose Detection, Video Annotation Application, Training CNN for Yoga Poses Recognition
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT24128,
author = "Ji\v{r}\'{i} Kut\'{a}lek",
type = "Bachelor's thesis",
title = "Detekce j\'{o}gov\'{y}ch pozic v obraze",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2021,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/24128/"
}