NDLI: Learning sparse shape bases for human action recognition

Content Provider	IEEE Xplore Digital Library
Author	Moayedi, F. Dashti, S.E. Boostani, R. Azimifar, Z.
Copyright Year	2015
Description	Author affiliation: Comput. Sci. & Eng. Dept., Shiraz Univ., Shiraz, Iran (Moayedi, F.; Boostani, R.; Azimifar, Z.) \|\| Dept. of Comput. Eng., Islamic Azad Univ., Jahrom, Iran (Dashti, S.E.)
Abstract	Human action recognition from image sequences is a challenging issue in the field of computer vision. In this paper we propose a new approach based on bag of words (BoW) framework. In this way, a video is modeled as a sequence of visual words, where human pose corresponds to a “word”, followed by a codebook is obtained by a famous unsupervised feature learning method, called sparse coding. The main advantages of applying sparse coding method to our framework are intrinsic high level bases extraction while reduction vector quantization error. In sparse coding approach, due to overcompletness characteristic of basis sets, scaling these methods to high-resolution data is computationally expensive. In order to address this problem, the main contribution of this work is to apply sparse coding on a set of shape filter banks which are estimated via multi-resolution decomposition methods such as Empirical Mode Decomposition (EMD) and Principal Components Analysis (PCA). In this way, the number of bases is dependent to the size of the filter banks instead of the input image size. The projected coefficients are integrated by temporal max pooling to generate the final representation. In classification stage the linear kernel SVM operated on sparse coding statistics achieved satisfying accuracy. We evaluate our method on the KTH, Weismann and UCF-sports human action datasets. The achieved results are either comparable to, or significantly better than previous presented results on these datasets.
Starting Page	755
Ending Page	760
File Size	683718
Page Count	6
File Format	PDF
e-ISBN	9781479919727
DOI	10.1109/IranianCEE.2015.7146314
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-10
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrical engineering Human action recognition Empirical mode decomposition Conferences Unsupervised features learning Eigen-poses Intrinsic mode function Sparse coding Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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