Olivares Poggi, Cesar AugustoHuiza Pereyra, Eric Raphael2020-09-012020-09-0120202020-08-31http://hdl.handle.net/20.500.12404/16906People with deafness or hearing disabilities who aim to use computer based systems rely on state-of-art video classification and human action recognition techniques that combine traditional movement pat-tern recognition and deep learning techniques. In this work we present a pipeline for semi-automatic video annotation applied to a non-annotated Peru-vian Signs Language (PSL) corpus along with a novel method for a progressive detection of PSL elements (nSDm). We produced a set of video annotations in-dicating signs appearances for a small set of nouns and numbers along with a labeled PSL dataset (PSL dataset). A model obtained after ensemble a 2D CNN trained with movement patterns extracted from the PSL dataset using Lucas Kanade Opticalflow, and a RNN with LSTM cells trained with raw RGB frames extracted from the PSL dataset reporting state-of-art results over the PSL dataset on signs classification tasks in terms of AUC, Precision and Recall.enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/pe/Redes neuronales (Computación)Algoritmos computacionalesReconocimiento óptico de patronesinfo:eu-repo/semantics/masterThesishttps://purl.org/pe-repo/ocde/ford#1.02.00