NDLI: Local descriptions for human action recognition from 3D reconstruction data

Content Provider	IEEE Xplore Digital Library
Author	Papadopoulos, Georgios Th. Daras, Petros
Copyright Year	2014
Description	Author affiliation: Information Technologies Institute, Centre for Research and Technology Hellas, Greece (Papadopoulos, Georgios Th.; Daras, Petros)
Abstract	In this paper, a view-invariant approach to human action recognition using 3D reconstruction data is proposed. Initially, a set of calibrated Kinect sensors are employed for producing a 3D reconstruction of the performing subjects. Subsequently, a 3D flow field is estimated for every captured frame. For performing action recognition, the ‘Bag-of-Words’ methodology is followed, where SpatioTemporal Interest Points (STIPs) are detected in the 4D space (xyz-coordinates plus time). A novel local-level 3D flow descriptor is introduced, which among others incorporates spatial and surface information in the flow representation and efficiently handles the problem of defining 3D orientation at every STIP location. Additionally, typical 3D shape descriptors of the literature are used for producing a more complete representation. Experimental results as well as comparative evaluation using datasets from the Huawei/3DLife 3D human reconstruction and action recognition Grand Challenge demonstrate the efficiency of the proposed approach.
Starting Page	2814
Ending Page	2818
File Size	287390
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025569
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Vectors Shape Estimation Videos Computer vision Noise 3D flow Action recognition view-invariance 3D reconstruction Kinect
Content Type	Text
Resource Type	Article

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