MoLa InCar AR: Dataset for Action Recognitiondoi:10.34622/datarepositorium/1S8QVPRepositório de Dados da Universidade do Minho2022-06-022R. P. Rodrigues, Nelson; M. C. da Costa, Nuno; Novais, Rita; Fonseca, Jaime; Cardoso, Paulo; Borges, Joao, 2022, "MoLa InCar AR: Dataset for Action Recognition", https://doi.org/10.34622/datarepositorium/1S8QVP, Repositório de Dados da Universidade do Minho, V2, UNF:6:t/LCTlQafeWpztygw8cACQ== [fileUNF]MoLa InCar AR: Dataset for Action Recognitiondoi:10.34622/datarepositorium/1S8QVPR. P. Rodrigues, NelsonM. C. da Costa, NunoNovais, RitaFonseca, JaimeCardoso, PauloBorges, JoaoR. P. Rodrigues, NelsonR. P. Rodrigues, NelsonR. P. Rodrigues, NelsonR. P. Rodrigues, NelsonR. P. Rodrigues, NelsonR. P. Rodrigues, NelsonUniversity of Minho, Guimaraes, PortugalPD/BDE/150500/2019Repositório de Dados da Universidade do MinhoR. P. Rodrigues, NelsonR. P. Rodrigues, Nelson2022-05-31Computer and Information ScienceEngineeringartificial intelligencecomputer visionaction recognitionWe introduce a dataset to train human action recognition inside the vehicle focusing on violence detection. The dataset was recorded with RGB, Depth, Thermal and Event-based data. Results: The resulting dataset contains 6 400 video samples and more than 3 million frames, collected from sixteen distinct subjects. The dataset contains 58 action classes, including violent and neutral (i.e., non-violent) activities.CC0 Waiver10.1007/s00138-020-01131-zBorges, J., Queirós, S., Oliveira, B. et al. (2021). A system for the generation of in-car human body pose datasets. Machine Vision and Applications, 32.10.5220/0009316205500557Borges, J., Oliveira, B., Torres, H. et al. (2020). Automated Generation of Synthetic in-Car Dataset for Human Body Pose Detection. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2; ISSN 2184-4321, pages 550-557.subjects_info.tab165text/tab-separated-valuesUNF:6:t/LCTlQafeWpztygw8cACQ==SubjectUNF:6:/5nA4VfzAESmYxBv1Pw/CQ==Height(cm)185.0.160.08.23179810248040616.0167.00.0169.8125UNF:6:0vU6VWvHtHFrtP+rUyEVow==Age16.05.81377674149945325.042.027.0.0.030.25UNF:6:JDkQFpRw5PveTiA6nEtzgg==SkinTone(Fitzpatrickscale)0.016.02.18751.04.0.2.01.1086778913041726UNF:6:oF3eMmj7yOcA7jIPgtSiBg==CountryUNF:6:eoBsBXYJ8apv1ACUZAzrNQ==InCar_GT_annotations.jsonapplication/jsonInCar_MetaData.txttext/plainINCAR.zip.001application/octet-streamINCAR.zip.002application/octet-streamINCAR.zip.003application/octet-streamINCAR.zip.004application/octet-streamINCAR.zip.005application/octet-streamINCAR.zip.006application/octet-streamINCAR.zip.007application/octet-streamINCAR.zip.008application/octet-streamINCAR.zip.009application/octet-streamINCAR.zip.010application/octet-streamINCAR.zip.011application/octet-streamINCAR.zip.012application/octet-streamINCAR.zip.013application/octet-streamINCAR.zip.014application/octet-streamINCAR.zip.015application/octet-streamINCAR.zip.016application/octet-streamINCAR.zip.017application/octet-streamINCAR.zip.018application/octet-streamINCAR.zip.019application/octet-streamINCAR.zip.020application/octet-streamINCAR.zip.021application/octet-streamINCAR.zip.022application/octet-streamINCAR.zip.023application/octet-stream