CROWD ANALYTICS VIA ONE SHOT LEARNING AND AGENT BASED INFERENCE

For the purposes of inferring social behavior in crowded conditions, three concepts have been explored: 1) the GE Sher-lock system which makes use of computer vision algorithms for the purposes of the opportunistic capture of various of social cues, 2) a one shot learning paradigm where behaviors can be identified based on as few as a single example and 3) an agent based approach to inference where generative models become the basis for social behavior recognition. The Sher-lock system makes use of tracking, facial analysis, gaze estimation and upper body motion analysis. The one-shot learning paradigm makes use of semantically meaningful affects as descriptors. The agent based inference methods allows for the incorporation of cognitive models as a basis for inference.