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http://hdl.handle.net/2445/182804
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DC Field | Value | Language |
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dc.contributor.advisor | Clapés, Albert | - |
dc.contributor.advisor | Escalera Guerrero, Sergio | - |
dc.contributor.author | Yuste Ramos, Joaquim | - |
dc.date.accessioned | 2022-01-31T10:16:51Z | - |
dc.date.available | 2022-01-31T10:16:51Z | - |
dc.date.issued | 2021-06-20 | - |
dc.identifier.uri | http://hdl.handle.net/2445/182804 | - |
dc.description | Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Albert Clapés i Sergio Escalera Guerrero | ca |
dc.description.abstract | [en] This project focuses on video action segmentation task, which aims to temporally segment and classify fine-grained actions in untrimmed videos. The development and refinement of this process is an important yet challenging problem, which can provide great improvements in work areas such as robotics, e-Health assistive technologies, surveillance, and beyond. On the one hand, we will study the current state-of-the-art, as well as the metrics that are commonly used to evaluate an architecture on this kind of problems. On the other hand, we introduce two different attention-based modules that are capable of extracting frame-to-frame relationships, and a behaviour analysis will be performed by evaluating them over Georgia Tech Egocentric Activity (GTEA), which is an outstanding dataset. This dataset is focused on daily cooking activity videos, with fine-grained labels, and it has an egocentric point view. Eventually, we will compare the obtained results against the actual state-of-the-art scores, in order to discuss the effectiveness of each module. | ca |
dc.format.extent | 45 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.rights | memòria: cc-nc-nd (c) Joaquim Yuste Ramos, 2021 | - |
dc.rights | codi: MIT License (c) Joaquim Yuste Ramos, 2021 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | - |
dc.rights.uri | https://opensource.org/licenses/MIT | * |
dc.source | Treballs Finals de Grau (TFG) - Enginyeria Informàtica | - |
dc.subject.classification | Aprenentatge automàtic | ca |
dc.subject.classification | Visió per ordinador | ca |
dc.subject.classification | Programari | ca |
dc.subject.classification | Treballs de fi de grau | ca |
dc.subject.classification | Xarxes neuronals convolucionals | ca |
dc.subject.classification | Reconeixement de formes (Informàtica) | ca |
dc.subject.classification | Xarxes neuronals (Informàtica) | ca |
dc.subject.other | Machine learning | en |
dc.subject.other | Computer vision | en |
dc.subject.other | Computer software | en |
dc.subject.other | Convolutional neural networks | en |
dc.subject.other | Pattern recognition systems | en |
dc.subject.other | Bachelor's theses | en |
dc.subject.other | Neural networks (Computer science) | en |
dc.title | Using deep learning for fine-grained action segmentation | ca |
dc.type | info:eu-repo/semantics/bachelorThesis | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Programari - Treballs de l'alumnat Treballs Finals de Grau (TFG) - Enginyeria Informàtica |
Files in This Item:
File | Description | Size | Format | |
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codi.zip | Codi font | 222.96 MB | zip | View/Open |
tfg_joaquim_yuste_ramos.pdf | Memòria | 2.02 MB | Adobe PDF | View/Open |
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