NDLI: Advances in human action recognition: an updated survey

Content Provider	IET Digital Library
Author	Bakar, Syed A. R. Abu
Abstract	Research in human activity recognition (HAR) has seen tremendous growth and continuously receiving attention from both the Computer Vision and the Image Processing communities. Due to the existence of numerous publications in this field, undoubtedly, there have been a number of review papers on this subject that categorise these techniques. Many of the recent works have started to tackle more challenging problems and these proposed techniques are addressing more realistic real-world scenarios. Conspicuously, an updated survey that covers these methods is timely due. To simplify the categorisation, this study takes a two-layer hierarchical approach. At the top level, the categorisation is based on the basic process flow of HAR, i.e. input data-type, features-type, descriptor-type, and classifier-type. At the second layer, each of these components is further subcategorised based on the diversity of the proposed methods. Finally, a remark on the coming popularity of deep learning approach in this field is also given.
Starting Page	2381
Ending Page	2394
Page Count	14
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 13, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0350
Journal	IET Image Processing
Publisher Date	2019-08-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Basic Process Flow Categorisation Categorise Classifier-type Computer Vision Computer Vision And Image Processing Technique Descriptor-type Feature Extraction Feature-type HAR Human Action Recognition Human Activity Recognition Image Motion Analysis Image Processing Communities Image Recognition Input Data-type Knowledge Engineering Technique Learning in AI Numerous Publications Review Papers Two-layer Hierarchical Approach Updated Survey
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Survey on deep learning methods in human action recognition

Meta-action descriptor for action recognition in RGBD video

Face recognition based on the fusion of global and local HOG features of face images

Calculation of phase fraction in steel microstructure images using random forest classifier

Fusing directional wavelet local binary pattern and moments for human action recognition

Learning pairwise SVM on hierarchical deep features for ear recognition

Multi-mode neural network for human action recognition

Face recognition algorithm based on feature descriptor and weighted linear sparse representation

Pain intensity recognition via multi-scale deep network

Advances in human action recognition: an updated survey

Similar Documents

Survey on deep learning methods in human action recognition

Meta-action descriptor for action recognition in RGBD video

Face recognition based on the fusion of global and local HOG features of face images

Calculation of phase fraction in steel microstructure images using random forest classifier

Fusing directional wavelet local binary pattern and moments for human action recognition

Learning pairwise SVM on hierarchical deep features for ear recognition

Multi-mode neural network for human action recognition

Face recognition algorithm based on feature descriptor and weighted linear sparse representation

Pain intensity recognition via multi-scale deep network

Advances in human action recognition: an updated survey