Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
The Aware Home: A Living Laboratory for Ubiquitous Computing Research
CoBuild '99 Proceedings of the Second International Workshop on Cooperative Buildings, Integrating Information, Organization, and Architecture
Skin-Color Modeling and Adaptation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Object Tracking with an Adaptive Color-Based Particle Filter
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Real-Time Online Adaptive Gesture Recognition
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised clustering of ambulatory audio and video
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Fusion of static and dynamic body biometrics for gait recognition
IEEE Transactions on Circuits and Systems for Video Technology
Multimodal human-computer interaction: A survey
Computer Vision and Image Understanding
IEEE Transactions on Information Technology in Biomedicine
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In this paper, we propose a multimodal system for detecting human activity and interaction patterns in a nursing home. Activities of groups of people are firstly treated as interaction patterns between any pair of partners and are then further broken into individual activities and behavior events using a multi-level context hierarchy graph. The graph is implemented using a dynamic Bayesian network to statistically model the multi-level concepts. We have developed a coarse-to-fine prototype system to illustrate the proposed concept. Experimental results have demonstrated the feasibility of the proposed approaches. The objective of this research is to automatically create concise and comprehensive reports of activities and behaviors of patients to support physicians and caregivers in a nursing facility.