Trabajo Fin de Máster
Clasificación de Actividades Humanas en Vídeo
Título alternativo | Human Action Recognition with Deep Learning |
Autor/es | Tatbak, Emre |
Director | Martínez del Amor, Miguel Ángel |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2020-07 |
Fecha de depósito | 2020-10-05 |
Titulación | Universidad de Sevilla. Máster Universitario en Lógica, Computación e Inteligencia Artificial |
Resumen | Nowadays, self-learning models and artificial intelligence are popular. These systems can be seen in daily life almost in every field. Artificial intelligence makes our life easier than we expected before. Now we can drive ... Nowadays, self-learning models and artificial intelligence are popular. These systems can be seen in daily life almost in every field. Artificial intelligence makes our life easier than we expected before. Now we can drive safer and easier with self-driving cars, we can predict our monthly expenses, in medical usage we can predict cancer cells with machine learning and also many other applications. Neural network is an effective tool for image recognition by computer vision algorithms. They work similar to human brain neural systems to recognize objects, their locations and also they can classify within multiple objects. With this project we will see how we can detect human actions on video camera with deep learning models. Mainly our goal is train a neural network model to recognize human activities on video and live camera. Our project has two stages; firstly only human body detection in all video, then using this video clip as the input of our deep learning model. Finally we classify the actions during all video. |
Cita | Tatbak, E. (2020). Clasificación de Actividades Humanas en Vídeo. (Trabajo Fin de Máster Inédito). Universidad de Sevilla, Sevilla. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Master_thesis_Emre_Tatbak.pdf | 2.207Mb | [PDF] | Ver/ | Documento principal |