Data-Free Prior Model for Upper Body Pose Estimation and Tracking

Video based human body pose estimation seeks to estimate the human body pose from an image or a video sequence, which captures a person exhibiting some activities. To handle noise and occlusion, a pose prior model is often constructed and is subsequently combined with the pose estimated from the ima...

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Vydáno v:IEEE transactions on image processing Ročník 22; číslo 12; s. 4627 - 4639
Hlavní autoři: Chen, Jixu, Nie, Siqi, Ji, Qiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.12.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Applied sciences
Benchmarking
Biological system modeling
Biomechanical Phenomena - physiology
Body pose estimation
body pose model
Data models
Databases, Factual
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Human body
Humans
Image processing
Image Processing, Computer-Assisted - methods
Information, signal and communications theory
Knowledge management
knowledge-based model
Models, Biological
Motion perception
Motion pictures
Pose estimation
Posture - physiology
Signal and communications theory
Signal processing
Signal, noise
Small mammals
Telecommunications and information theory
Three dimensional
Tracking
Training data
Transaction processing
Performance evaluation
Target tracking
knowledge-based model
body pose model
Video signal
Knowledge base
Video recording
Learning
Biomechanics
Human body model
Body pose estimation
Image sequence
Position measurement
Signal processing
Motion detection
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:Video based human body pose estimation seeks to estimate the human body pose from an image or a video sequence, which captures a person exhibiting some activities. To handle noise and occlusion, a pose prior model is often constructed and is subsequently combined with the pose estimated from the image data to achieve a more robust body pose tracking. Various body prior models have been proposed. Most of them are data-driven, typically learned from 3D motion capture data. In addition to being expensive and time-consuming to collect, these data-based prior models cannot generalize well to activities and subjects not present in the motion capture data. To alleviate this problem, we propose to learn the prior model from anatomic, biomechanics, and physical constraints, rather than from the motion capture data. For this, we propose methods that can effectively capture different types of constraints and systematically encode them into the prior model. Experiments on benchmark data sets show the proposed prior model, compared with data-based prior models, achieves comparable performance for body motions that are present in the training data. It, however, significantly outperforms the data-based prior models in generalization to different body motions and to different subjects.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2013.2274748