@book{
 title = {Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition},
 type = {book},
 year = {2019},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 keywords = {Convolutional Neural Network,Sign language,Spatial Temporal Graph},
 volume = {11731 LNCS},
 id = {f0498f0c-a302-3d57-b314-ff26a3941e60},
 created = {2019-10-14T23:59:00.000Z},
 file_attached = {false},
 profile_id = {74e7d4ea-3dac-3118-aab9-511a5b337e8f},
 last_modified = {2021-01-14T07:56:00.742Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© Springer Nature Switzerland AG 2019. The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network for sign language recognition based on the human skeletal movements. The method uses graphs to capture the dynamics of the signs in two dimensions, spatial and temporal, considering the complex aspects of the language. Additionally, we present a new dataset of human skeletons for sign language based on ASLLVD to contribute to future related studies.},
 bibtype = {book},
 author = {de Amorim, C.C. and Macêdo, D. and Zanchettin, C.},
 doi = {10.1007/978-3-030-30493-5_59}
}

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