NDLI: Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation

Content Provider	Springer Nature Link
Author	Sigal, Leonid Isard, Michael Haussecker, Horst Black, Michael J.
Copyright Year	2011
Abstract	We formulate the problem of 3D human pose estimation and tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loosely-connected body-parts. In particular, we model the body using an undirected graphical model in which nodes correspond to parts and edges to kinematic, penetration, and temporal constraints imposed by the joints and the world. These constraints are encoded using pair-wise statistical distributions, that are learned from motion-capture training data. Human pose and motion estimation is formulated as inference in this graphical model and is solved using Particle Message Passing (PaMPas). PaMPas is a form of non-parametric belief propagation that uses a variation of particle filtering that can be applied over a general graphical model with loops. The loose-limbed model and decentralized graph structure allow us to incorporate information from “bottom-up” visual cues, such as limb and head detectors, into the inference process. These detectors enable automatic initialization and aid recovery from transient tracking failures. We illustrate the method by automatically tracking people in multi-view imagery using a set of calibrated cameras and present quantitative evaluation using the HumanEva dataset.
Starting Page	15
Ending Page	48
Page Count	34
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	98
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2011-09-30
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Articulated pose estimation Articulated tracking Human pose estimation Human motion tracking Non-parametric belief propagation Computer Imaging, Vision, Pattern Recognition and Graphics Pattern Recognition Image Processing and Computer Vision Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation

HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion

Multi-view 3D Human Pose Estimation in Complex Environment

2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images

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Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

Coupled Visual and Kinematic Manifold Models for Tracking

3D Human Pose Tracking Priors using Geodesic Mixture Models

Coupled Action Recognition and Pose Estimation from Multiple Views

Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation

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