Interactive concepts for shaping generative models of spatial behavior

Abstract

A technique widely used in video based situation assessment, and especially in anomaly detection, is the analysis of spatial behavior in terms of motion profiles recorded along trajectories. An intuitive assessment metric is the deviation from normal behavior, where generative models are a natural choice for capturing the underlying statistics. Applying such outlier methods in open world scenarios has the drawback that long observation times are required, in order to fully determine the model, while underdetermined models are very prone to generate non-intuitive or wrong results. In order to address this problem, the usage of interactive concepts for supporting the learning process and refining learned models is proposed. Thereby, the method keeps track of automatically integrated observations and stochastic priors generated by examples provided by the user. Examples can be given in terms of individual labeled samples, or in terms of complex pdfs. The feasibility of the proposed approach is illustrated on the BIWI Walking Pedestrians dataset, using partitioned Gaussian mixture models as the generative model.