This talk will describe a variety of methods that have been developed for the purposes of understanding group level social behaviors using stand-off video surveillance methods. Three main topics are considered: 1) the GE Sherlock System: a comprehensive approach to capturing and analyzing non-verbal cues of persons in crowd/group level interactions, 2) One Shot Learning: a new approach to crowd level behavior recognition based on the concept that a new behavior can be recognized with as little as a single example and 3) Agent Based Inference: a novel approach to the analysis of individual cognitive states of person?s interacting in a group or crowd level social interactions. The talk starts with a description of the GE Sherlock system which encompasses methods such as person tracking in crowds, dynamic PTZ camera control, facial analytics from a distance such as gaze estimation and expression recognition, upper body affective pose analysis and the inference of social states such as rapport and hostility. The talk then discusses how cues derived from the Sherlock system can be used to construct semantically meaningful behavior descriptors or affects allowing for signature matching between behaviors which can be viewed as a form of one shot learning. Going beyond affects based on direct observation, we argue that more meaningful affects can be constructed via the inference of the cognitive states of each individual. To this end we introduce the Agent Based Inference framework. The talk concludes with a discussion of how such methods are making their way into commercial use via efforts such as the intelligent city, the intelligent airport and the intelligent hospital.
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