Bayesian Bio-inspired Model for Learning Interactive Trajectories

Automatic understanding of human behavior is an important and challenging objective in several surveillance applications. One of the main problems of this task consists in accurately defining models able to characterize in a discriminative but, at the same time, enough general way people actions. In this work a bio-inspired model is proposed to represent people interactions in a Bayesian framework using their patterns of movement. Couples of observed interacting trajectories are encoded into a Dynamic Bayesian Network (DBN) model where states and conditional probability densities are learned in an online manner in order to statistically describe interactions. Observed trajectories are processed by the Instantaneous Topological Map (ITM) algorithm that automatically creates a topological map used to define the states of the DBN. The transition probabilities are estimated by combining states frequency of occurrence, evaluated by a voting-based approach, and their temporal occurrence represented by Gaussian Mixture Models. The discriminative capabilities of this model to detect interactions are shown both in a simulated and in a real-world environment.