NDLI: Action recognition using weighted three-state Hidden Markov Model

Content Provider	IEEE Xplore Digital Library
Author	Ning Li De Xu
Copyright Year	2008
Description	Author affiliation: Inst. of Comput. Sci. & Eng., Beijing Jiaotong Univ., Beijing (Ning Li; De Xu)
Abstract	Hidden Markov Model (HMM) based human action recognition (HAR) has been broadly adopted by HAR community. However, existing works donpsilat pay attention to the relationship between the layout of the model and the property of human action. In this paper, a novel HAR method is proposed based on the assumption that human action can be essentially recognized by three key postures located around the initial, middle and terminal action period. Rested on this hypothesis, we improve the HMM-based HAR method. The main contributions are twofold: (1) human action is modeled as non-return three-state HMM; (2) output probabilities in each state are weighted in terms of the key postures position in an action period. Experiments on publicly available human action database show the approach constitutes a suggestive proof for the close relationship between human action property and the design of HMM for HAR task.
Starting Page	1428
Ending Page	1431
File Size	114707
Page Count	4
File Format	PDF
ISBN	9781424421787
DOI	10.1109/ICOSP.2008.4697400
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Humans Histograms Lifting equipment Computer science Electronic mail Databases Skeleton Image motion analysis Computer vision HMM action recognition weighted output probability Semi-Connected HMM Full-Connected
Content Type	Text
Resource Type	Article

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

Human activity recognition: Various paradigms

Human action recognition with line and flow histograms

HMM-based human motion recognition with optical flow data

Face recognition based on separable lattice 2-D HMM with state duration modeling

Human action recognition using multi-view image sequences

View-invariant human-body detection with extension to human action recognition using component-wise HMM of body parts

HMM-based Human Action Recognition Using Multiview Image Sequences

Human actions recognition from streamed Motion Capture

Recognition of handwritten dates on bank checks using an HMM approach

Action recognition using weighted three-state Hidden Markov Model

Similar Documents

Human activity recognition: Various paradigms

Human action recognition with line and flow histograms

HMM-based human motion recognition with optical flow data

Face recognition based on separable lattice 2-D HMM with state duration modeling

Human action recognition using multi-view image sequences

View-invariant human-body detection with extension to human action recognition using component-wise HMM of body parts

HMM-based Human Action Recognition Using Multiview Image Sequences

Human actions recognition from streamed Motion Capture

Recognition of handwritten dates on bank checks using an HMM approach

Action recognition using weighted three-state Hidden Markov Model