Show simple item record

dc.contributorBoza Rocho, Santiago
dc.contributorSánchez Riera, Jordi
dc.contributor.authorAlmasi, Mohammad
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.coverage.spatialeast=2.1155387; north=41.38288; name=Carrer Adolf Florensa, 12, 08028 Barcelona, Espanya
dc.date.accessioned2020-07-31T09:23:13Z
dc.date.issued2020-07
dc.identifier.urihttp://hdl.handle.net/2117/328142
dc.description.abstractUnderstanding human movements and recognizing them in different categories is always challenging for many applications; from humanoid and assistive robots to medical rehabilitation. Furthermore, considering the different viewpoints of the camera makes this field a unique phenomenon. This project aims to understand and distinguish different actions from the viewpoint of egocentric camera. Firstly, in this experiment , the Blender environment has been used to build the human motion dataset; with the use of two different (small and large) datasets respectively. There are four and fifteen different actions, consisting of 5K and 120K different frames captured from human movements of different ages. Secondly, the optical flow of each scenario was calculated. Thirdly, these feature vectors have been applied to the long short term memory (LSTM) neural network architecture to classify different actions. The accuracy of the results for a small section of dataset with four actions is close to 94% and for the large dataset with fifteen actions is near 83%. This type of experiment has many applications, especially in rehabilitation and biomechanics.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshArtificial intelligence
dc.subject.otherHuman action recognition
dc.subject.otherBlender software
dc.subject.otherEgocentric cameras
dc.subject.otherLSTM
dc.subject.otherNeural network
dc.subject.otherDeep learning
dc.subject.otherOptical flow
dc.subject.otherSports analysis.
dc.titleUndestanding human motions from ego-camera videos
dc.typeMaster thesis
dc.subject.lemacIntel·ligència artificial
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.identifier.slugFME-1934
dc.rights.accessRestricted access - confidentiality agreement
dc.date.lift10000-01-01
dc.date.updated2020-07-17T09:24:42Z
dc.audience.educationlevelMàster
dc.audience.mediatorUniversitat Politècnica de Catalunya. Facultat de Matemàtiques i Estadística
dc.audience.degreeMÀSTER UNIVERSITARI EN MATEMÀTICA AVANÇADA I ENGINYERIA MATEMÀTICA (Pla 2010)
dc.contributor.covenanteeInstitut de Robòtica i Informàtica Industrial


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain