NDLI: 3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis

Content Provider	IEEE Xplore Digital Library
Author	Moon, K. Pavlovic, V.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ (Moon, K.; Pavlovic, V.)
Abstract	We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic (PLSA) models to describe the mapping of image features to 3D human pose estimates. PLSA has been successfully used to model the co-occurrence of dyadic data on problems such as image annotation where image features are mapped to word categories via latent variable semantics. We apply the PLSA approach to motion tracking by extending it to a sequential setting where the latent variables describe intrinsic motion semantics linking human figure appearance to 3D pose estimates. This dynamic PLSA (DPLSA) approach is in contrast to many current methods that directly learn the often high-dimensional image-to-pose mappings and utilize subspace projections as a constraint on the pose space alone. As a consequence, such mappings may often exhibit increased computational complexity and insufficient generalization performance. We demonstrate the utility of the proposed model on the synthetic dataset and the task of 3D human motion tracking in monocular image sequences with arbitrary camera views. Our experiments show that the proposed approach can produce accurate pose estimates at a fraction of the computational cost of alternative subspace tracking methods.
Starting Page	155
Ending Page	162
File Size	978722
Page Count	8
File Format	PDF
ISBN	9780769531533
DOI	10.1109/CRV.2008.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	shared latent space Tracking Motion estimation Subspace constraints dynamic probabilistic latent semantic analysis Humans Image sequences Computational complexity Image motion analysis motion tracking Gaussian process latent variable model Cameras Motion analysis Joining processes
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

3d human motion tracking using dynamic probabilistic latent semantic analysis.

3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis

Monocular 3d human motion tracking using dynamic probabilistic latent semantic analysis.

3D Footwear Model Matching System using Two Cameras

Dynamic visual motion estimation from subspace constraints

Tracking 3D coplanar points in the invariant perspective coordinates plane

Visual Tracking Based on 3D Probabilistic Reconstruction

Modeling human locomotion with topologically constrained latent variable models

Multi-view tracking of articulated human motion in silhouette and pose manifolds

3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis

Similar Documents

3d human motion tracking using dynamic probabilistic latent semantic analysis.

3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis

Monocular 3d human motion tracking using dynamic probabilistic latent semantic analysis.

3D Footwear Model Matching System using Two Cameras

Dynamic visual motion estimation from subspace constraints

Tracking 3D coplanar points in the invariant perspective coordinates plane

Visual Tracking Based on 3D Probabilistic Reconstruction

Modeling human locomotion with topologically constrained latent variable models

Multi-view tracking of articulated human motion in silhouette and pose manifolds

3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis