Title: Integrating depth-HOG and spatio-temporal joints data for action recognition
Authors: Arora, Noopur
Shukla, Parul
Biswas, Kanad K.
Citation: WSCG '2016: short communications proceedings: The 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 30 - June 3 2016, p. 245-252.
Issue Date: 2016
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2016/!!_CSRN-2602.pdf
ISBN: 978-80-86943-58-9
ISSN: 2464-4617
Keywords: rozpoznávání akce;hluboké HOG;kynetika;data o těle a spojích
Keywords in different language: action recognition;depth-HOG;kinectics;body-joints data
Abstract: In this paper, we propose an approach for human activity recognition using gradient orientation of depth maps and spatio-temporal features from body-joints data. Our approach is based on an amalgamation of key local and global feature descriptors such as spatial pose, temporal variation in ‘joints’ position and spatio-temporal gradient orientation of depth maps. Additionally, we obtain a motion-induced global shape feature describing the motion dynamics during an action. Feature selection is carried out to select a relevant subset of features for action recognition. The resultant features are evaluated using SVM classifier. We validate our proposed method on our own dataset consisting of 11 classes and a total of 287 videos. We also compare the effectiveness of our method on the MSR-Action3D dataset.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2016: Short Papers Proceedings

Files in This Item:
File Description SizeFormat 
Arora.pdfPlný text604,7 kBAdobe PDFView/Open

Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/29710

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.