NDLI: Action recognition using fast HOG3D of integral videos and Smith

Content Provider	IET Digital Library
Author	Henawy, Ibrahim El Ahmed, Kareem Mahmoud, Hamdi
Abstract	Recognising human activity from video stream has become one of the most interesting applications in computer vision. In this study, a novel hybrid technique for human action recognition is proposed based on fast HOG3D of integral videos and Smith–Waterman partial shape matching of the fused frame. The proposed technique is divided into two main stages, the first stage extracts a set of foreground snippets from the input video, and extracts the histogram of 3D gradient orientations from the spatio-temporal volumetric data; and the second stage fuses a set of key frames from current snippet and extracts the contours from the fused frame. Non-linear support vector machine (SVM) decision trees are used to classify HOG3D features into one of fixed action categories. On the other hand, Smith–Waterman partial shape matching is used to compare between the contour of the fused frame and the stored template contour of specified action. The results from SVM and Smith–Waterman partial shape matching are then combined. The experimental results show that combining non-linear SVM decision trees of HOG3D features and Smith–Waterman partial shape matching of fused contours improved the accuracy of the classification model while maintaining efficiency in time elapsed for training.
Starting Page	896
Ending Page	908
Page Count	13
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 6, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0627
Journal	IET Image Processing
Publisher Date	2018-01-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Gradient Orientation Combinatorial Mathematics Computer Vision Computer Vision And Image Processing Technique Decision Tree Fast HOG3D Feature Classification Feature Extraction Frame Fusion Human Action Recognition Human Activity Recognition Image Classification Image Fusion Image Matching Image Recognition Integral Videos Knowledge Engineering Technique Nonlinear Support Vector Machine Decision Tree Nonlinear SVM Decision Tree Smith-Waterman Partial Shape Matching Spatiotemporal Volumetric Data Support Vector Machine Video Stream
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

Realistic human action recognition by Fast HOG3D and self-organization feature map

Fast Support Vector Machine Classification using linear SVMs

Action recognition by discriminative EdgeBoxes

Multi-aircrafts tracking using spatial–temporal constraints-based intra-frame scale-invariant feature transform feature matching

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

Dimensionality reduction of Fisher vectors for human action recognition

Feature Fusion Hierarchies for gender classification

Two-layer discriminative model for human activity recognition

Fusing image representations for classification using support vector machines

Action recognition using fast HOG3D of integral videos and Smith–Waterman partial matching

Similar Documents

Realistic human action recognition by Fast HOG3D and self-organization feature map

Fast Support Vector Machine Classification using linear SVMs

Action recognition by discriminative EdgeBoxes

Multi-aircrafts tracking using spatial–temporal constraints-based intra-frame scale-invariant feature transform feature matching

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

Dimensionality reduction of Fisher vectors for human action recognition

Feature Fusion Hierarchies for gender classification

Two-layer discriminative model for human activity recognition

Fusing image representations for classification using support vector machines

Action recognition using fast HOG3D of integral videos and Smith–Waterman partial matching