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dc.contributor.authorAl-Akam, Rawya
dc.contributor.authorAl-Darraji, Salah
dc.contributor.authorPaulus, Dietrich
dc.contributor.editorSkala, Václav
dc.date.accessioned2019-05-10T10:05:29Z-
dc.date.available2019-05-10T10:05:29Z-
dc.date.issued2018
dc.identifier.citationWSCG 2018: poster papers proceedings: 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 1-7.en
dc.identifier.isbn978-80-86943-42-8
dc.identifier.issn2464-4617
dc.identifier.uriwscg.zcu.cz/WSCG2018/!!_CSRN-2803.pdf
dc.identifier.urihttp://hdl.handle.net/11025/34631
dc.description.abstractHuman action recognition from the videos is one of the most attractive topics in computer vision during the last decades due to wide applications development. This research has mainly focused on learning and recognizing actions from RGB and Depth videos (RGBD). RGBD is a powerful source of data providing the aligned depth information which has great ability to improve the performance of different problems in image understanding and video processing. In this work, a novel system for human action recognition is proposed to extract distinctive spatio and temporal feature vectors for presenting the spatio-temporal evolutions from a set of training and testing video sequences of different actions. The feature vectors are computed in two steps: The First step is the motion detection from all video frames by using spatio-temporal retina model. This model gives a good structuring of video data by removing the noise and illumination variation and is used to detect potentially salient areas, these areas represent the motion information of the moving object in each frame of video sequences. In the Second step, because of human motion can be seen as a type of texture pattern, the local binary pattern descriptor (LBP) is used to extract features from the spatio-temporal salient areas and formulated them as a histogram to make the bag of feature vectors. To evaluate the performance of the proposed method, the k-means clustering, and Random Forest classification is applied on the bag of feature vectors. This approach is demonstrated that our system achieves superior performance in comparison with the state-of-the-art and all experimental results are depending on two public RGBD datasets.en
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.relation.ispartofseriesWSCG 2018: poster papers proceedingsen
dc.rights© Václav Skala - Union Agencycs
dc.subjectrozpoznání akcecs
dc.subjectRGBD videacs
dc.subjectlokální binární vzorycs
dc.subjectmodel sítnicecs
dc.subjectnáhodný lescs
dc.titleHuman action recognition from RGBD videos based on retina model and local binary pattern featuresen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedaction recognitionen
dc.subject.translatedRGBD videosen
dc.subject.translatedlocal binary patternen
dc.subject.translatedretina modelen
dc.subject.translatedrandom foresten
dc.identifier.doihttps://doi.org/10.24132/CSRN.2018.2803.1
dc.type.statusPeer-revieweden
Appears in Collections:WSCG 2018: Poster Papers Proceedings

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