Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Integration of Local Image Cues for Probabilistic 2D Pose Recovery
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Recovery of upper body poses in static images based on joints detection
Pattern Recognition Letters
International Journal of Computer Vision
Bottom-up recognition and parsing of the human body
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Boosted multiple deformable trees for parsing human poses
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Segmentation of human body parts using deformable triangulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Monocular human pose tracking using multi frame part dynamics
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Efficient inference with multiple heterogeneous part detectors for human pose estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
A two-stage Bayesian network method for 3D human pose estimation from monocular image sequences
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Integrating multiple uncalibrated views for human 3D pose estimation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Computer Vision and Image Understanding
Human posture analysis under partial self-occlusion
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Predicting 3d people from 2d pictures
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Human pose tracking using multi-level structured models
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Contextual and skin color region information for face and arms location
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images
International Journal of Computer Vision
Combination of annealing particle filter and belief propagation for 3D upper body tracking
Applied Bionics and Biomechanics - Personal Care Robotics
| Hi-index | 0.00 |
We propose a statistical formulation for 2-D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to infer pose parameters from image cues such as appearance, shape, edge, and color. From a set of hand labeled images, we accumulate prior knowledge of 2-D body shapes by learning their low-dimensional representations for inference of pose parameters. A data driven belief propagation Monte Carlo algorithm, utilizing importance sampling functions built from bottom-up visual cues, is proposed for efficient probabilistic inference. Contrasted to the few sequential statistical formulations in the literature, our algorithm integrates both top-down as well as bottom-up reasoning mechanisms, and can carry out the inference tasks in parallel. Experimental results demonstrate the potency and effectiveness of the proposed algorithm in estimating 2-D human pose from single images.