Uncertainty Modeling Framework for Constraint-Based Elementary Scenario Detection in Vision Systems

Abstract

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 semantics. Ontology and description-based approaches can explicitly embed scene semantics, but their deterministic nature is susceptible to noise from underlying components of vision systems. We propose a probabilistic framework to handle uncertainty on a constraint-based ontology 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) as input. Two evaluations were carried out: the first, a 3-fold cross-validation focusing on elementary scenario detection (n:10 participants); 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: 84 to 100 %) and precision indices ( e.g., In zone Reading: 54.5 to 85.7%), and the recall of Complex scenarios.