NDLI: Human action recognition using histogram of motion intensity and direction from multiple views

Content Provider	IET Digital Library
Author	Chun, Sung Yong Lee, Chan Su
Abstract	This study presents a human action recognition system from multi-view image sequences. The authors’ approach to human action recognition is based on an estimation of local motion from multiple camera views. The authors propose a new motion descriptor, called histogram of motion intensity and direction, to capture local motion characteristics of human activity. After image normalisation, they estimate motion flow using dense optical flow. Using regular grids, they extract local flow motion and estimate the dominant angle and the intensity of optical flow. The histogram of the dominant angle and its intensity are used as a descriptor for each sequence. After the identification of head direction, they concatenate descriptors in each view as a single feature vector from multiple-view sequences. Classification based on the proposed feature vector using support vector machine shows better performance than three-dimensional optical flow-based approaches, but with lower computational requirements. The authors evaluated action recognition on the publicly available i3DPost and the Institut de Recherche en Informatique et en Automatique (INRIA) Xmas Motion Acquisition Sequences database. Experimental results show promising state-of-the-art results and validate the advanced performance of the authors’ approach.
Starting Page	250
Ending Page	257
Page Count	8
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 4, Jun (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0233
Journal	IET Computer Vision
Publisher Date	2016-02-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Computer Vision And Image Processing Technique Dense Optical Flow Feature Vector Head Direction Human Action Recognition System Human Activity I3DPost Image Normalisation Image Recognition Image Sequence INRIA Knowledge Engineering Technique Local Flow Motion Local Motion Estimation Motion Descriptor Motion Direction Motion Estimation Motion Intensity Multiple Camera Views Multiview Image Sequence Single Feature Vector Support Vector Machine SVM Three-dimensional Optical Flow-based Approach Vector
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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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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