Recognizing interactions in video

Abstract

Detection and tracking algorithms generates useful information in the form of trajectories from which the behaviors and the interactions of moving objects can be inferred through the analysis of spatio-temporal features. Interactions occur either between a dynamic and a static object, or between multiple dynamic objects. This chapter presents an interaction modeling framework formulated as a state sequence estimation problem using time-series analysis. Bayesian network-based methods and their variants are studied for the analysis of interactions in videos. Moreover, techniques such as Coupled Hidden Markov Model are also discussed for more complex interactions, such as those between multiple dynamic objects. Finally, the interaction modeling is demonstrated on real surveillance and sport sequences. © 2010 Springer-Verlag Berlin Heidelberg.