Motion-Based View-Invariant Articulated Motion Detection and Pose Estimation Using Sparse Point Features

Authors:
Shrinivas J. Pundlik;Stanley T. Birchfield
Affiliations:
Clemson University, Clemson, USA;Clemson University, Clemson, USA
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Year:
2009

Citing 12
Cited 1

Robust Real-Time Periodic Motion Detection, Analysis, and Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection

International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
The Recognition of Human Movement Using Temporal Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Detection and Tracking of Human Motion with a View-Based Representation

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning and Recognizing Human Dynamics in Video Sequences

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Unsupervised Learning of Human Motion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Priors for People Tracking from Small Training Sets

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Unsupervised Bayesian Detection of Independent Motion in Crowds

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle

International Journal of Computer Vision
Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

International Journal of Computer Vision
Human pose tracking using multi-level structured models

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III

Estimating pose of articulated objects using low-level motion

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an approach for articulated motion detection and pose estimation that uses only motion information. To estimate the pose and viewpoint we introduce a novel motion descriptor that computes the spatial relationships of motion vectors representing various parts of the person using the trajectories of a number of sparse points. A nearest neighbor search for the closest motion descriptor from the labeled training data of human walking poses in multiple views is performed. This observational probability is fed to a Hidden Markov Model defined over multiple poses and viewpoints to obtain temporally consistent pose estimates. Experimental results on various sequences of walking subjects with multiple viewpoints demonstrate the effectiveness of the approach. In particular, our purely motion-based approach is able to track people even when other visible cues are not available, such as in low-light situations.