Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Analysis of Human Locomotion based on Partial Measurements
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Actions Sketch: A Novel Action Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Recovering Human Body Configurations Using Pairwise Constraints between Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Model-Based Approach for Estimating Human 3D Poses in Static Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Patient monitoring in medical applications such as diagnosis of sleep disorders commonly adopts invasive monitoring equipments such as pulse oximetry and polysomnogram (PSG), but their attachment to the patient's body disturb sleep and therefore compromise results. Furthermore, the invasive approaches often fail to monitor continuously because the devices can be pulled off by the subject during sleep unconsciously. This paper presents an automated noninvasive video monitoring approach to analyze (covered) human activity in conditions with persistent heavy occlusion. The proposed method is a model-based approach, employing both static shape features and dynamic motion features to suppress false positive detection, to identify human activity, and to self-improve the covered human pose estimation.