Clasificación de vídeos mediante Redes Neuronales Artificiales
[EN] Nowadays, the research on computer vision and machine learning is in its best moment. The computational capacity and communications currently available in any device, have risen new challenges. Among them, the task of human or object recognition on images and video are impulsed by the best univ...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/64848 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/64848 |
| Access Level: | acceso abierto |
| Palabra clave: | Visión por computador Aprendizaje automático Redes neuronales artificiales Reconocimiento de la actividad humana Computer vision Machine learning Artificial neural networks Human activity recognition LENGUAJES Y SISTEMAS INFORMATICOS Máster Universitario en Inteligencia Artificial, Reconocimiento de Formas e Imagen Digital-Màster Universitari en Intel·ligència Artificial, Reconeixement de Formes i Imatge Digital |
| Sumario: | [EN] Nowadays, the research on computer vision and machine learning is in its best moment. The computational capacity and communications currently available in any device, have risen new challenges. Among them, the task of human or object recognition on images and video are impulsed by the best universities and technological companies. Concretely, human activity recognition in videos has a direct application in many environments: security systems, interaction analysis, illness identification, etc. For this reason, this project proposes a prospective study about the task of THUMOS competition on computer vision. In this task, it is required to classify videos by activity, among a set of 101 activities, belonging to 5 different kinds: Human-Human interaction, Human-Object interaction, sports, body-motion, and playing musical instruments. This thesis proposes, applied to this task for the first time, a model based on artificial neural networks that uses improved Dense Trajectories as a feature extraction technique. This thesis will analize the current state-of-the-art, and it will perform experiments in order to obtain the best model for this task, and afterwards, these experiments will be compared with the results provided by the approaches on the top ten of the THUMOS classification |
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