Maintaining knowledge about temporal intervals
Communications of the ACM
Automatic video interpretation: a novel algorithm for temporal scenario recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An Event-driven Context Model in Elderly Health Monitoring
UIC-ATC '09 Proceedings of the 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An Activity Monitoring System for Real Elderly at Home: Validation Study
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
A Framework Dealing with Uncertainty for Complex Event Recognition
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Recognizing complex events using large margin joint low-level event model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
A Generic Framework for Video Understanding Applied to Group Behavior Recognition
AVSS '12 Proceedings of the 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
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We herein present a hierarchical model-based framework for event recognition using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipment) with moving objects (e.g., a Person) detected by a monitoring system. The event models follow a generic ontology based on natural language, which allows domain experts to easily adapt them. The framework novelty relies on combining multiple sensors at decision (event) level, and handling their conflict using a probabilistic approach. The proposed approach for event conflict handling computes the event reliability for each sensor, and then combines them using Dempster-Shafer Theory with an alternative combination rule. The proposed framework is evaluated using multi-sensor recording of instrumental daily living activities (e.g., watching TV, writing a check, preparing tea, organizing week intake of prescribed medication) of participants of a clinical trial for Alzheimer's disease. Two evaluation cases are presented: the combination of events (or activities) from heterogeneous sensors (RGB ambient camera and a wearable inertial sensor) by a deterministic fashion, and the combination of conflicting events recognized by video cameras with partially overlapped field of view (a RGB- and a RGB-D-camera, Kinect®). The results show the framework improves the event recognition rate in both cases.