W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4S: A real-time system detecting and tracking people in 2 1/2D
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Learning When Negative Examples Abound
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Recognition of Human Activity through Hierarchical Stochastic Learning
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Hydra: Multiple People Detection and Tracking Using Silhouettes
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Boosted Classification Trees and Class Probability/Quantile Estimation
The Journal of Machine Learning Research
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Sensor-Based Human Activity Recognition in a Multi-user Scenario
AmI '09 Proceedings of the European Conference on Ambient Intelligence
DETECTION OF SOCIAL INTERACTION IN SMART SPACES
Cybernetics and Systems - SOCIAL AWARENESS IN SMART SPACES: PART I
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
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Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In real life there will be situations where the inhabitant receives visits from family members or professional health care givers. In such cases activity recognition is unreliable. In this paper, we investigate the problem of detecting multiple persons in an environment equipped with a sensor network consisting of binary sensors. We conduct a real-life experiment for detection of visits in the office of the supervisor where the office is equipped with a video camera to record the ground truth. We collected data during two months and used two models, a Naive Bayes Classifier and a Hidden Markov Model for a visitor detection. An evaluation of these two models shows that we achieve an accuracy of 83% with the NBC and an accuracy of 92% with a HMM, respectively.