Event detection has advanced significantly in the past decades relying on pixel- and feature-level representations of video-clips. Although effective those representations have difficulty on incorporating scene se- mantics. Ontology and description-based approaches can explicitly em- bed scene semantics, but their deterministic nature is susceptible to noise from underlying components of vision systems. We propose a proba- bilistic framework to handle uncertainty on a constraint-based ontol- ogy framework for event detection. This work focuses on elementary event (scenario) uncertainty and proposes probabilistic constraints to quantify the spatial relationship between person and contextual objects. The uncertainty modeling framework is demonstrated on the detection of activities of daily living of participants of an Alzheimer's disease study, monitored by a vision system using a RGB-D sensor (Kinect , Microsoft c ) as input. Two evaluations were carried out: the first, a 3- fold cross-validation focusing on elementary scenario detection (n:10 par- ticipants); and the second devoted for complex scenario detection (semi- probabilistic approach, n:45). Results showed the uncertainty modeling improves the detection of elementary scenarios in recall (e.g., In zone phone: 85 to 100 %) and precision indices (e.g., In zone Reading: 54.71 to 73.15%), and the recall of Complex scenarios. Future work will extend the uncertainty modeling for composite event level.