NDLI: UGraSP: A unified framework for activity recognition and person identification using graph signal processing

Content Provider	IEEE Xplore Digital Library
Author	Batabyal, T. Vaccari, A. Acton, S.T.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Univ. of Virginia., Charlottesville, VA, USA (Batabyal, T.; Vaccari, A.; Acton, S.T.)
Abstract	With the growing availability and wide distribution of low-cost, high-performance 3D imaging sensors, the image analysis community has witnessed an increased demand for solutions to the challenges of activity recognition and person identification. We propose an integrated framework, based on graph signal processing, that simultaneously performs both tasks using a single set of features. The novelty of our approach is based on the fact that the set of features used for activity recognition accommodates person identification without additional computation. The analysis is based on the extracted structure-invariant graph (skeleton). The Laplacian of the skeleton is used both to identify the person and recognize the performed activity. While person identification is achieved directly from the analysis of the Laplacian, activity recognition is obtained after transformation, into the graph spectral domain, of the vectorized form of the skeletal joints 3D coordinates. Feature vectors for activity recognition are then derived, in this domain, from the covariance matrices evaluated over fixed-length sequential video segments. Both classification tasks are implemented using linear support vector machines (SVM). When applied to real activity datasets, our approach shows an improved performance over the existing state-of-the-art.
Starting Page	3270
Ending Page	3274
File Size	1158955
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351408
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laplace equations Skeleton Three-dimensional displays Motion segmentation Image recognition Sensors Support vector machines Point cloud datasets Laplacian Adjacency Matrix Graph Signal Processing Graph Fourier Transform activity Recognition Person Identification
Content Type	Text
Resource Type	Article

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