A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
ACM Computing Surveys (CSUR)
Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Experiments on Eigenfaces Robustness
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Representation and Recognition of Events in Surveillance Video Using Petri Nets
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
Adaptive Tracking of Non-Rigid Objects Based on Color Histograms and Automatic Parameter Selection
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A Computer Vision System for Monitoring Medication Intake
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of skin-color modeling and detection methods
Pattern Recognition
Face and Hands Detection and Tracking Applied to the Monitoring of Medication Intake
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
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This paper presents a computer vision system for monitoring medication intake in the context of home care services. We use a method based on color and shape to detect the body parts and the medication bottles. Color is used for skin detection, and the shape is used to distinguish the face from the hands and to differentiate bottles of medicine. To track these objects, we use a method based on color histograms, Hu moments, and edges. For the recognition of medication intake, we use a Petri network and event recognition. Our method has an accuracy of more than 75% and allows the detection of the medication intake in various scenarios where the user is cooperative.