Interaction Analysis with a Bayesian Trajectory Model

Human behavior recognition is one of the most important and challenging objectives performed by intelligent vision systems. Several issues must be faced in this domain ranging from the extraction of reliable and meaningful features from video streams, to the accurate definition of models able to characterize in a discriminative but, at the same time, general enough way people actions. In this work to cope with these problems a Dynamic Bayesian Network (DBN) model is proposed to recognize people interactions using their patterns of movement. Couples of observed interacting trajectories are processed by the Instantaneous Topological Map (ITM) algorithm to automatically create a topological map used to define the states of the DBN. The trajectories are encoded into the model where conditional probability densities are learned in order to statistically describe interactions. Finally, an online cumulative measure is defined to recognize interactions both in simulated and real-world environ...

• Type: Journal
• Year: 2010
• Where: EXPERT
• Authors: Alessio Dore, Carlo S. Regazzoni
• Keywords: Computer Networks, Dynamic Bayesian Networks, EXPERT 2010, Instantaneous Topological Map, Topological Map