Supporting generative models of spatial behavior by user interaction

The analysis of spatial behavior in terms of motion profiles recorded along trajectories is a widely used technique in video analysis. Inherent to this approach is the problem to assign a meaningful score to observations. This score builds the basis for classification, ranking, or to generate user feedback. Score assignment can be done in terms of deviations from normal behavior, where normality is determined by learning a generative model. A general drawback is that the unsupervised learning process often assigns non-intuitive scores. In order to address this problem this paper proposes the usage of interactive concepts, which support the learning process. Interaction thereby strongly utilizes the generative models capabilities to synthesize samples, to give insight into the underlying representation. Initial results are shown on a trajectory rating task, illustrating the feasibility of the proposed approach.