<?xml version='1.0' encoding='UTF-8'?><codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>MoLa InVicon AR: Dataset for Action Recognition</titl><IDNo agency="DOI">doi:10.34622/datarepositorium/WWUTUT</IDNo></titlStmt><distStmt><distrbtr source="archive">Repositório de Dados da Universidade do Minho</distrbtr><distDate>2022-06-07</distDate></distStmt><verStmt source="DVN"><version date="2022-06-14" type="RELEASED">2</version></verStmt><biblCit>R. P. Rodrigues, Nelson; M. C. da Costa, Nuno; Novais, Rita; Fonseca, Jaime; University of Minho; Borges, Joao, 2022, "MoLa InVicon AR: Dataset for Action Recognition", https://doi.org/10.34622/datarepositorium/WWUTUT, Repositório de Dados da Universidade do Minho, V2</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>MoLa InVicon AR: Dataset for Action Recognition</titl><IDNo agency="DOI">doi:10.34622/datarepositorium/WWUTUT</IDNo></titlStmt><rspStmt><AuthEnty affiliation="University of Minho">R. P. Rodrigues, Nelson</AuthEnty><AuthEnty affiliation="University of Minho">M. C. da Costa, Nuno</AuthEnty><AuthEnty affiliation="University of Minho">Novais, Rita</AuthEnty><AuthEnty affiliation="University of Minho">Fonseca, Jaime</AuthEnty><AuthEnty affiliation="University of Minho">University of Minho</AuthEnty><AuthEnty affiliation="University of Minho">Borges, Joao</AuthEnty></rspStmt><prodStmt/><distStmt><distrbtr source="archive">Repositório de Dados da Universidade do Minho</distrbtr><contact affiliation="University of Minho">R. P. Rodrigues, Nelson</contact><depositr>R. P. Rodrigues, Nelson</depositr><depDate>2022-06-03</depDate></distStmt></citation><stdyInfo><subject><keyword>Computer and Information Science</keyword><keyword>Engineering</keyword></subject><abstract>This system is then spatially and temporally calibrated with the camera sensor, automatically obtaining image data with (multi-person) pose annotations (2D and 3D). Finally, we demonstrate the feasibility of using the system’s generated data (which was made publicly available), this dataset for human action recognition focuses on violence and normal (non-violent) actions inside the vehicle, which is composed of 16 subjects and contains 6 400 video samples and more than 3.5 million frames. 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