A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect User Activity and Availability from a Variety of Sensor Data
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Review: Ambient intelligence: Technologies, applications, and opportunities
Pervasive and Mobile Computing
Annotating smart environment sensor data for activity learning
Technology and Health Care - Smart Environments: Technology to Support Healthcare
Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Predicting air quality in smart environments
Journal of Ambient Intelligence and Smart Environments
Learning patterns in ambient intelligence environments: a survey
Artificial Intelligence Review
Expert Systems with Applications: An International Journal
Sensor selection to support practical use of health-monitoring smart environments
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A survey on ontologies for human behavior recognition
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
This paper addresses the detection of activities of individuals in a smart home environment. Our system is based on a robust video tracker that creates and tracks targets using a wide-angle camera. The system uses target position, size and orientation as input for interpretation. Interpretation produces activity labels such as "walking", "standing", "sitting", "interacting with table", or "sleeping" for each target. Bayesian Classifier and Support Vector Machines (SVMs) are compared for learning and recognizing previously defined individual activities. These methods are evaluated on recorded data sets. A novel Hybrid Classifier is then proposed. This classifier combines generative Bayesian methods and discriminative SVMs. Bayesian methods are used to detect previously unseen activities, while the SVMs are shown to provide high discriminative power for recognizing examples of learned activity classes. The evaluation results of the Hybrid classifier for the recorded data sets show that the combination of generative and discriminative classification methods outperforms the individual methods when identifying unseen activities.